diff --git a/.github/workflows/test_latest_versions.yml b/.github/workflows/test_latest_versions.yml
index e8baf7489..03e079878 100644
--- a/.github/workflows/test_latest_versions.yml
+++ b/.github/workflows/test_latest_versions.yml
@@ -46,9 +46,4 @@ jobs:
         run: |
           pver=${{ matrix.python-version }}
           tox -epy${pver/./} -- --run-slow
-          notebook_flags=""
-          if [ "$pver" = "3.12" ]; then
-            echo Skipping tutorials that require cplex
-            notebook_flags="${notebook_flags} --ignore=docs/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb"
-          fi
-          tox -epy${pver/./}-notebook -- ${notebook_flags}
+          tox -epy${pver/./}-notebook
diff --git a/CITATION.bib b/CITATION.bib
index f098dff50..7f90b42a3 100644
--- a/CITATION.bib
+++ b/CITATION.bib
@@ -12,10 +12,7 @@ @misc{circuit-knitting-toolbox
     and Edwin Pednault
     and C. D. Pemmaraju
     and Pedro Rivero
-    and Seetharami Seelam
     and Ibrahim Shehzad
-    and Dharmashankar Subramanian
-    and Wei Tang
     and Stefan Woerner
   },
   title = {{Circuit Knitting Toolbox}},
diff --git a/INSTALL.rst b/INSTALL.rst
index 068f40e2b..0d84681b5 100644
--- a/INSTALL.rst
+++ b/INSTALL.rst
@@ -9,18 +9,8 @@ packages. There are three primary ways to do this:
 - :ref:`Option 2`
 - :ref:`Option 3`
 
-CutQC users should consult the :ref:`Platform Support` section to determine
-which installation option is appropriate for them. Users who wish to run within a
-containerized environment may skip the pre-installation and move straight
-to :ref:`Option 3`.
-
-.. note::
-
-    If a user wishes to use the circuit cutting toolbox to
-    automatically find optimized wire cuts for a circuit too large for
-    the free version of CPLEX, they should acquire a license and install
-    the `full
-    version <https://www.ibm.com/products/ilog-cplex-optimization-studio>`__.
+Users who wish to run within a containerized environment may skip the
+pre-installation and move straight to :ref:`Option 3`.
 
 Pre-Installation
 ^^^^^^^^^^^^^^^^
@@ -61,14 +51,6 @@ Upgrade pip and install the CKT package.
     pip install --upgrade pip
     pip install circuit-knitting-toolbox
 
-Users intending to use the automatic cut finding functionality in the CutQC package should install the ``cplex`` optional dependency.
-
-Adjust the options below to suit your needs.
-
-.. code:: sh
-    
-    pip install 'circuit-knitting-toolbox[cplex]'
-
 
 .. _Option 2:
 
@@ -94,13 +76,11 @@ The next step is to install CKT to the virtual environment. If you plan on runni
 notebook dependencies in order to run all the visualizations in the notebooks.
 If you plan on developing in the repository, you may want to install the ``dev`` dependencies.
 
-Users intending to use the automatic cut finding functionality in the CutQC package should install the ``cplex`` optional dependency.
-
 Adjust the options below to suit your needs.
 
 .. code:: sh
     
-    pip install tox notebook -e '.[notebook-dependencies,dev,cplex]'
+    pip install tox notebook -e '.[notebook-dependencies,dev]'
 
 If you installed the notebook dependencies, you can get started with CKT by running the notebooks in the docs.
 
@@ -166,27 +146,6 @@ the only one that will be saved across different container runs.
 Platform Support
 ^^^^^^^^^^^^^^^^
 
-Users of Mac M1 or M2 chips and Windows users may have issues running certain components of CKT.
-
-If you are using Linux or macOS with an Intel chip (i.e., not the
-new M1 or M2 chips), everything should work natively, so we
-recommend either :ref:`Option 1` or :ref:`Option 2`.
-
-All users on ARM chips, as well as all Windows users, may have to
-take care when installing the toolbox, depending on which tools they
-intend to use.
-  
-  - The automatic wire cut search in the ``cutqc`` package depends
-    on CPLEX, which is only available on Intel chips and is not yet available
-    for Python 3.12.
-
-In each case, one method that is guaranteed to work is to :ref:`use
-the toolbox within Docker <Option 3>`.  Other methods include:
-
-  - Users on Apple's M series of chips may wish to install an x86
-    version of Python.  For instance, `conda
-    <https://docs.conda.io/en/latest/miniconda.html>`__ users can run
-    ``CONDA_SUBDIR=osx-64 conda create -n x86_venv python=3`` to
-    create a virtual environment that uses Python compiled for the x86
-    instruction set.  No matter the installation method, there is a
-    performance cost due to emulation.
+We expect this package to work on `any platform supported by Qiskit <https://docs.quantum.ibm.com/start/install#operating-system-support>`__. If
+you are experiencing issues running the software on your device, you
+may consider :ref:`using the toolbox within Docker <Option 3>`.
diff --git a/README.md b/README.md
index d7a592ade..be88bb943 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@
 Circuit Knitting is the process of decomposing a larger quantum circuit into many smaller circuits, executing those circuits on a quantum processor(s), and then knitting their results into a reconstruction of the original circuit's outcome.
 
 The toolbox currently contains the following tools:
-- Circuit Cutting [[1-6]](#references)
+- Circuit Cutting [[1-5]](#references)
  
 For a more detailed discussion on circuit cutting, check out our [technical guide](https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/explanation/index.html#overview-of-circuit-cutting).
 ----------------------------------------------------------------------------------------------------
@@ -45,10 +45,10 @@ All CKT documentation is available at https://qiskit-extensions.github.io/circui
   
 ### Installation
 
-We encourage installing CKT via ``pip``, when possible. Users intending to use the automatic cut finding functionality in the ``CutQC`` package should install the ``cplex`` optional dependency.
+We encourage installing CKT via ``pip``, when possible.
 
 ```bash
-pip install 'circuit-knitting-toolbox[cplex]'
+pip install 'circuit-knitting-toolbox'
 ```
 
 For information on installing from source, running CKT in a container, and platform support, refer to the [installation instructions](https://qiskit-extensions.github.io/circuit-knitting-toolbox/install.html) in the CKT documentation.
@@ -71,9 +71,7 @@ This project is meant to evolve rapidly and, as such, does not follow [Qiskit's
 
 [4] Lukas Brenner, Christophe Piveteau, David Sutter, [Optimal wire cutting with classical communication](https://arxiv.org/abs/2302.03366), arXiv:2302.03366 [quant-ph].
 
-[5] Wei Tang, Teague Tomesh, Martin Suchara, Jeffrey Larson, Margaret Martonosi, [CutQC: Using small quantum computers for large quantum circuit evaluations](https://doi.org/10.1145/3445814.3446758), Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. pp. 473 (2021).
-  
-[6] K. Temme, S. Bravyi, and J. M. Gambetta, [Error mitigation for short-depth quantum circuits](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509), Physical Review Letters, 119(18), (2017).
+[5] K. Temme, S. Bravyi, and J. M. Gambetta, [Error mitigation for short-depth quantum circuits](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509), Physical Review Letters, 119(18), (2017).
   
 ----------------------------------------------------------------------------------------------------
 
diff --git a/circuit_knitting/cutting/__init__.py b/circuit_knitting/cutting/__init__.py
index ae08e1056..bb015ef47 100644
--- a/circuit_knitting/cutting/__init__.py
+++ b/circuit_knitting/cutting/__init__.py
@@ -76,21 +76,6 @@
     qpd.generate_qpd_weights
     qpd.decompose_qpd_instructions
     qpd.qpdbasis_from_instruction
-
-CutQC
-=====
-
-.. autosummary::
-   :toctree: ../stubs/
-   :nosignatures:
-
-    cutqc.run_subcircuit_instances
-    cutqc.generate_summation_terms
-    cutqc.build
-    cutqc.verify
-    cutqc.cut_circuit_wires
-    cutqc.evaluate_subcircuits
-    cutqc.reconstruct_full_distribution
 """
 
 from .cutting_decomposition import (
diff --git a/circuit_knitting/cutting/cutqc/__init__.py b/circuit_knitting/cutting/cutqc/__init__.py
deleted file mode 100644
index 4e9c45b7e..000000000
--- a/circuit_knitting/cutting/cutqc/__init__.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""Code to initialize the cutqc imports."""
-
-from warnings import warn
-
-from .wire_cutting_evaluation import run_subcircuit_instances
-from .wire_cutting_post_processing import generate_summation_terms, build
-from .wire_cutting_verification import verify
-from .wire_cutting import (
-    cut_circuit_wires,
-    evaluate_subcircuits,
-    reconstruct_full_distribution,
-    create_dd_bin,
-    reconstruct_dd_full_distribution,
-)
-
-__all__ = [
-    "run_subcircuit_instances",
-    "generate_summation_terms",
-    "build",
-    "verify",
-    "cut_circuit_wires",
-    "evaluate_subcircuits",
-    "reconstruct_full_distribution",
-    "create_dd_bin",
-    "reconstruct_dd_full_distribution",
-]
-
-warn(
-    f"The package ``{__name__}`` is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0. "
-    "The circuit cutting workflow in ``circuit_knitting.cutting`` now implements similar and improved functionalities, which will be maintained going forward."
-    " See https://qiskit-extensions.github.io/circuit-knitting-toolbox/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb for a tutorial on the newly added automated cut-finding functionality.",
-    DeprecationWarning,
-    stacklevel=2,
-)
diff --git a/circuit_knitting/cutting/cutqc/dynamic_definition.py b/circuit_knitting/cutting/cutqc/dynamic_definition.py
deleted file mode 100644
index b14f0e02b..000000000
--- a/circuit_knitting/cutting/cutqc/dynamic_definition.py
+++ /dev/null
@@ -1,541 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-# The code in this file was ported from CutQC at https://github.com/weiT1993/CutQC
-# (C) Copyright 2022 Wei Tang (weit@princeton.edu), released under the MIT License:
-#
-#   MIT License
-#
-#   Copyright (c) 2021 weiT1993
-#
-#   Permission is hereby granted, free of charge, to any person obtaining a copy
-#   of this software and associated documentation files (the "Software"), to deal
-#   in the Software without restriction, including without limitation the rights
-#   to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-#   copies of the Software, and to permit persons to whom the Software is
-#   furnished to do so, subject to the following conditions:
-#
-#   The above copyright notice and this permission notice shall be included in all
-#   copies or substantial portions of the Software.
-#
-#   THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-#   IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-#   FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-#   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-#   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-#   OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-#   SOFTWARE.
-
-
-"""Functions of Dynamic Definition for CutQC."""
-
-from __future__ import annotations
-
-import itertools
-import copy
-import numpy as np
-from concurrent.futures import ThreadPoolExecutor
-from typing import Sequence, Any
-from qiskit import QuantumCircuit
-from qiskit.circuit import Qubit
-from qiskit.utils.deprecation import deprecate_func
-
-from .wire_cutting_post_processing import build, find_process_jobs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def dd_build(
-    summation_terms: Sequence[dict[int, int]],
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]],
-    num_cuts: int,
-    mem_limit: int,
-    recursion_depth: int,
-    counter: dict[int, dict[str, int]],
-    subcircuit_instances: dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-    num_threads: int,
-) -> dict[int, dict[str, Any]]:
-    """
-    Create bins of dynamic definition.
-
-    Args:
-        summation_terms: The summation terms used to generate the full
-            vector, as generated in generate_summation_terms
-        subcircuit_entry_probs: The probabilities vectors from the
-            subcircuit executions
-        num_cuts: The number of cuts
-        mem_limit: Memory limit size
-        recursion_depth: The number of iteration for calculating bin
-        counter: The dictionary containing all meta information regarding
-            each of the subcircuits
-        subcircuit_instances: The dictionary containing the index information for each
-            of the subcircuit instances
-        num_threads: The number of threads to use for multithreading
-
-    Returns:
-        The bin for dynamic definition
-    """
-    dd_bins: dict[int, dict[str, Any]] = {}
-    dd_schedule: dict[str, dict[int, str]] = {}
-    overhead = {"additions": 0, "multiplications": 0}
-
-    num_qubits = sum([count["effective"] for count in counter.values()])
-    largest_bins: list[dict[str, Any]] = []
-    recursion_layer = 0
-
-    while recursion_layer < recursion_depth:
-        #  Get qubit states
-        if recursion_layer == 0:
-            dd_schedule = _initialize_dynamic_definition_schedule(counter, mem_limit)
-        elif len(largest_bins) == 0:
-            break
-        else:
-            bin_to_expand = largest_bins.pop(0)
-            dd_schedule = _next_dynamic_definition_schedule(
-                recursion_layer=bin_to_expand["recursion_layer"],
-                bin_id=bin_to_expand["bin_id"],
-                dd_bins=dd_bins,
-                mem_limit=mem_limit,
-            )
-
-        merged_subcircuit_entry_probs = _merge_states_into_bins(
-            subcircuit_instances, subcircuit_entry_probs, dd_schedule, num_threads
-        )
-        reconstructed_prob, smart_order, recursion_overhead = build(
-            summation_terms=summation_terms,
-            subcircuit_entry_probs=merged_subcircuit_entry_probs,
-            num_cuts=num_cuts,
-            num_threads=1,
-        )
-
-        #  Build from the merged subcircuit entries
-        overhead["additions"] += recursion_overhead["additions"]
-        overhead["multiplications"] += recursion_overhead["multiplications"]
-
-        dd_bins[recursion_layer] = dd_schedule
-        dd_bins[recursion_layer]["smart_order"] = smart_order
-        dd_bins[recursion_layer]["bins"] = reconstructed_prob
-        dd_bins[recursion_layer]["expanded_bins"] = []
-
-        #  Sort and truncate the largest bins
-        has_merged_states = False
-        for subcircuit_idx in dd_schedule["subcircuit_state"]:
-            if "merged" in dd_schedule["subcircuit_state"][subcircuit_idx]:
-                has_merged_states = True
-                break
-
-        if recursion_layer < recursion_depth - 1 and has_merged_states:
-            bin_indices = np.argpartition(reconstructed_prob, -recursion_depth)[
-                -recursion_depth:
-            ]
-            for bin_id in bin_indices:
-                if reconstructed_prob[bin_id] > 1 / 2**num_qubits / 10:
-                    largest_bins.append(
-                        {
-                            "recursion_layer": recursion_layer,
-                            "bin_id": bin_id,
-                            "prob": reconstructed_prob[bin_id],
-                        }
-                    )
-            largest_bins = sorted(
-                largest_bins, key=lambda bin: bin["prob"], reverse=True
-            )[:recursion_depth]
-
-        recursion_layer += 1
-    return dd_bins
-
-
-def _initialize_dynamic_definition_schedule(
-    counter: dict[int, dict[str, int]], mem_limit: int
-) -> dict[str, dict[int, str]]:
-    """
-    Create schedule to merge each simulation result in first iteration for DD.
-
-    Args:
-        counter: The dictionary containing all meta information regarding
-            each of the subcircuits
-        mem_limit: Memory limit size
-
-    Returns:
-        The schedule for first iteration of DD
-    """
-    schedule: dict = {}
-    schedule["subcircuit_state"] = {}
-    schedule["upper_bin"] = None
-
-    subcircuit_capacities = {
-        subcircuit_idx: counter[subcircuit_idx]["effective"]
-        for subcircuit_idx in counter
-    }
-    subcircuit_active_qubits = _distribute_load(
-        capacities=subcircuit_capacities, mem_limit=mem_limit
-    )
-    for subcircuit_idx in subcircuit_active_qubits:
-        num_zoomed = 0
-        num_active = subcircuit_active_qubits[subcircuit_idx]
-        num_merged = counter[subcircuit_idx]["effective"] - num_zoomed - num_active
-        schedule["subcircuit_state"][subcircuit_idx] = [
-            "active" for _ in range(num_active)
-        ] + ["merged" for _ in range(num_merged)]
-    return schedule
-
-
-def _next_dynamic_definition_schedule(
-    recursion_layer: int,
-    bin_id: int,
-    dd_bins: dict[int, dict[str, Any]],
-    mem_limit: int,
-) -> dict[str, dict[int, str]]:
-    """
-    Create schedule to merge each simulation result after second iteration for DD.
-
-    Args:
-        counter: The dictionary containing all meta information regarding
-            each of the subcircuits
-        mem_limit: Memory limit size
-
-    Returns:
-        The schedule after second iteration of DD
-    """
-    num_active = 0
-    for subcircuit_idx in dd_bins[recursion_layer]["subcircuit_state"]:
-        num_active += dd_bins[recursion_layer]["subcircuit_state"][
-            subcircuit_idx
-        ].count("active")
-
-    binary_bin_idx = bin(bin_id)[2:].zfill(num_active)
-    smart_order = dd_bins[recursion_layer]["smart_order"]
-    next_dd_schedule = {
-        "subcircuit_state": copy.deepcopy(dd_bins[recursion_layer]["subcircuit_state"])
-    }
-    binary_state_idx_ptr = 0
-
-    for subcircuit_idx in smart_order:
-        for qubit_ctr, qubit_state in enumerate(
-            next_dd_schedule["subcircuit_state"][subcircuit_idx]
-        ):
-            if qubit_state == "active":
-                next_dd_schedule["subcircuit_state"][subcircuit_idx][qubit_ctr] = int(
-                    binary_bin_idx[binary_state_idx_ptr]
-                )
-                binary_state_idx_ptr += 1
-    next_dd_schedule["upper_bin"] = (recursion_layer, bin_id)
-
-    subcircuit_capacities = {
-        subcircuit_idx: next_dd_schedule["subcircuit_state"][subcircuit_idx].count(
-            "merged"
-        )
-        for subcircuit_idx in next_dd_schedule["subcircuit_state"]
-    }
-    subcircuit_active_qubits = _distribute_load(
-        capacities=subcircuit_capacities, mem_limit=mem_limit
-    )
-
-    for subcircuit_idx in next_dd_schedule["subcircuit_state"]:
-        num_active = subcircuit_active_qubits[subcircuit_idx]
-        for qubit_ctr, qubit_state in enumerate(
-            next_dd_schedule["subcircuit_state"][subcircuit_idx]
-        ):
-            if qubit_state == "merged" and num_active > 0:
-                next_dd_schedule["subcircuit_state"][subcircuit_idx][
-                    qubit_ctr
-                ] = "active"
-                num_active -= 1
-        assert num_active == 0
-    return next_dd_schedule
-
-
-def _distribute_load(capacities: dict[int, int], mem_limit: int) -> dict[int, int]:
-    """
-    Determine the size to merge bit depending on memory size.
-
-    Args:
-        capacities: The number of bit that can be used
-        mem_limit: Memory limit size
-
-    Returns:
-        The number of each bit DD can use
-    """
-    total_load = min(sum(capacities.values()), mem_limit)
-    total_capacity = sum(capacities.values())
-    loads = {subcircuit_idx: 0 for subcircuit_idx in capacities}
-
-    for slot_idx in loads:
-        loads[slot_idx] = int(capacities[slot_idx] / total_capacity * total_load)
-    total_load -= sum(loads.values())
-
-    for slot_idx in loads:
-        while total_load > 0 and loads[slot_idx] < capacities[slot_idx]:
-            loads[slot_idx] += 1
-            total_load -= 1
-    assert total_load == 0
-    return loads
-
-
-def _merge_prob_vector(
-    unmerged_prob_vector: np.ndarray, qubit_states: str
-) -> np.ndarray:
-    """
-    Merge the probability vector.
-
-    Args:
-        umperged_prob_vector: Original probability vector
-        qubit_states: Indicate Merge or Active status
-    Returns:
-        The merged probability vector
-    """
-    num_active = qubit_states.count("active")
-    num_merged = qubit_states.count("merged")
-    merged_prob_vector = np.zeros(2**num_active, dtype="float32")
-
-    for active_qubit_states in itertools.product(["0", "1"], repeat=num_active):
-        if len(active_qubit_states) > 0:
-            merged_bin_id = int("".join(active_qubit_states), 2)
-        else:
-            merged_bin_id = 0
-        for merged_qubit_states in itertools.product(["0", "1"], repeat=num_merged):
-            active_ptr = 0
-            merged_ptr = 0
-            binary_state_id = ""
-            for qubit_state in qubit_states:
-                if qubit_state == "active":
-                    binary_state_id += active_qubit_states[active_ptr]
-                    active_ptr += 1
-                elif qubit_state == "merged":
-                    binary_state_id += merged_qubit_states[merged_ptr]
-                    merged_ptr += 1
-                else:
-                    binary_state_id += "%s" % qubit_state
-            state_id = int(binary_state_id, 2)
-            merged_prob_vector[merged_bin_id] += unmerged_prob_vector[state_id]
-
-    return merged_prob_vector
-
-
-def _merge_state_into_bins_parallel(
-    subcircuit_instances: dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]],
-    dd_schedule: dict[str, dict[int, str]],
-    num_workers: int,
-    rank: int,
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Merge the probability vector for each rank.
-
-    Args:
-        subcircuit_instances: The dictionary containing the index information for each
-            of the subcircuit instances
-        subcircuit_entry_probs: The probabilities vectors from the
-            subcircuit executions
-        dd_schedule: The schedule of DD
-        num_workers: The number of workers
-        rank: the number of ranks
-    Returns:
-        The merged probability vector
-    """
-    merged_subcircuit_entry_probs: dict[int, dict[int, np.ndarray]] = {}
-    for subcircuit_idx in subcircuit_instances:
-        merged_subcircuit_entry_probs[subcircuit_idx] = {}
-        rank_jobs = find_process_jobs(
-            jobs=list(subcircuit_instances[subcircuit_idx].keys()),
-            rank=rank,
-            num_workers=num_workers,
-        )
-        for subcircuit_entry_init_meas in rank_jobs:
-            subcircuit_entry_id = subcircuit_instances[subcircuit_idx][
-                subcircuit_entry_init_meas
-            ]
-            merged_subcircuit_entry_probs[subcircuit_idx][subcircuit_entry_id] = (
-                _merge_prob_vector(
-                    subcircuit_entry_probs[subcircuit_idx][subcircuit_entry_id],
-                    dd_schedule["subcircuit_state"][subcircuit_idx],
-                )
-            )
-    return merged_subcircuit_entry_probs
-
-
-def _merge_states_into_bins(
-    subcircuit_instances: dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]],
-    dd_schedule: dict[str, dict[int, str]],
-    num_workers: int,
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Submit tasks to the function that merges the probability vector for each rank.
-
-    Args:
-        subcircuit_instances: The dictionary containing the index information for each
-            of the subcircuit instances
-        subcircuit_entry_probs: The probabilities vectors from the
-            subcircuit executions
-        dd_schedule: The schedule of DD
-        num_workers: The number of workers
-    Returns:
-        The merged probability vector
-    """
-    #  The first merge of subcircuit probs using the target number of bins
-    #  Saves the overhead of writing many states in the first SM recursion
-    workers_merge_subcircuit_entry_probs = []
-
-    future_prob = []
-    executor = ThreadPoolExecutor(max_workers=num_workers)
-
-    for rank in range(num_workers):
-        future_prob.append(
-            executor.submit(
-                _merge_state_into_bins_parallel,
-                subcircuit_instances,
-                subcircuit_entry_probs,
-                dd_schedule,
-                num_workers,
-                rank,
-            )
-        )
-    for future in future_prob:
-        workers_merge_subcircuit_entry_probs.append(future.result())
-
-    merged_subcircuit_entry_probs = {}
-    for rank in range(num_workers):
-        rank_merged_subcircuit_entry_probs = workers_merge_subcircuit_entry_probs[rank]
-        for subcircuit_idx in rank_merged_subcircuit_entry_probs:
-            if subcircuit_idx not in merged_subcircuit_entry_probs:
-                merged_subcircuit_entry_probs[subcircuit_idx] = (
-                    rank_merged_subcircuit_entry_probs[subcircuit_idx]
-                )
-            else:
-                merged_subcircuit_entry_probs[subcircuit_idx].update(
-                    rank_merged_subcircuit_entry_probs[subcircuit_idx]
-                )
-    return merged_subcircuit_entry_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def read_dd_bins(
-    subcircuit_out_qubits: dict[int, list[int]], dd_bins: dict[int, dict[str, Any]]
-) -> np.ndarray:
-    """
-    Reconstruct a probability vector for the original circuit by DD's bin.
-
-    Args:
-        subcircuit_out_qubits: The order information of the probability
-            of the original circuit
-        dd_bins: The bin of DD
-    Returns:
-        The probability vector of the original circuit
-    """
-    num_qubits = sum(
-        [
-            len(subcircuit_out_qubits[subcircuit_idx])
-            for subcircuit_idx in subcircuit_out_qubits
-        ]
-    )
-    reconstructed_prob = np.zeros(2**num_qubits, dtype=np.float32)
-
-    for recursion_layer in dd_bins:
-        num_active = sum(
-            [
-                dd_bins[recursion_layer]["subcircuit_state"][subcircuit_idx].count(
-                    "active"
-                )
-                for subcircuit_idx in dd_bins[recursion_layer]["subcircuit_state"]
-            ]
-        )
-        for bin_id, bin_prob in enumerate(dd_bins[recursion_layer]["bins"]):
-            if bin_prob > 0 and bin_id not in dd_bins[recursion_layer]["expanded_bins"]:
-                binary_bin_id = bin(bin_id)[2:].zfill(num_active)
-                # print("dd bin %s" % binary_bin_id)
-                binary_full_state = ["" for _ in range(num_qubits)]
-                for subcircuit_idx in dd_bins[recursion_layer]["smart_order"]:
-                    subcircuit_state = dd_bins[recursion_layer]["subcircuit_state"][
-                        subcircuit_idx
-                    ]
-                    for subcircuit_qubit_idx, qubit_state in enumerate(
-                        subcircuit_state
-                    ):
-                        qubit_idx = subcircuit_out_qubits[subcircuit_idx][
-                            subcircuit_qubit_idx
-                        ]
-                        if qubit_state == "active":
-                            binary_full_state[qubit_idx] = binary_bin_id[0]
-                            binary_bin_id = binary_bin_id[1:]
-                        else:
-                            binary_full_state[qubit_idx] = "%s" % qubit_state
-                merged_qubit_indices = []
-                for qubit, qubit_state in enumerate(binary_full_state):
-                    if qubit_state == "merged":
-                        merged_qubit_indices.append(qubit)
-                num_merged = len(merged_qubit_indices)
-                average_state_prob = bin_prob / 2**num_merged
-                for binary_merged_state in itertools.product(
-                    ["0", "1"], repeat=num_merged
-                ):
-                    for merged_qubit_ctr in range(num_merged):
-                        binary_full_state[merged_qubit_indices[merged_qubit_ctr]] = (
-                            binary_merged_state[merged_qubit_ctr]
-                        )
-                    full_state = "".join(binary_full_state)[::-1]
-                    full_state_idx = int(full_state, 2)
-                    reconstructed_prob[full_state_idx] = average_state_prob
-    return reconstructed_prob
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def get_reconstruction_qubit_order(
-    full_circuit: QuantumCircuit,
-    complete_path_map: dict[Qubit, list[dict[str, int | Qubit]]],
-    subcircuits: list[QuantumCircuit],
-) -> dict[int, list[int]]:
-    """
-    Get the output qubit in the full circuit for each subcircuit.
-
-    Args:
-        full_circuit: The original quantum circuit
-        complete_path_map: The complete path through the subcircuits
-        subcircuits: The subcircuits
-    Returns:
-        The output qubit for the original circuit
-    """
-    subcircuit_out_qubits: dict[int, list[Qubit]] = {
-        subcircuit_idx: [] for subcircuit_idx in range(len(subcircuits))
-    }
-    for input_qubit in complete_path_map:
-        path = complete_path_map[input_qubit]
-        output_qubit = path[-1]
-        subcircuit_out_qubits[output_qubit["subcircuit_idx"]].append(
-            (
-                output_qubit["subcircuit_qubit"],
-                full_circuit.qubits.index(input_qubit),
-            )
-        )
-    for subcircuit_idx in subcircuit_out_qubits:
-        subcircuit_out_qubits[subcircuit_idx] = sorted(
-            subcircuit_out_qubits[subcircuit_idx],
-            key=lambda x: subcircuits[subcircuit_idx].qubits.index(x[0]),
-            reverse=True,
-        )
-        subcircuit_out_qubits[subcircuit_idx] = [
-            x[1] for x in subcircuit_out_qubits[subcircuit_idx]
-        ]
-    return subcircuit_out_qubits
diff --git a/circuit_knitting/cutting/cutqc/mip_model.py b/circuit_knitting/cutting/cutqc/mip_model.py
deleted file mode 100644
index 19927fde4..000000000
--- a/circuit_knitting/cutting/cutqc/mip_model.py
+++ /dev/null
@@ -1,540 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""File containing the tools to find and manage the cuts."""
-
-from __future__ import annotations
-
-from typing import Sequence, Any
-
-import numpy as np
-
-from qiskit.utils.deprecation import deprecate_func
-
-
-class MIPModel(object):
-    """
-    Class to contain the model that manages the cut MIP.
-
-    This class represents circuit cutting as a Mixed Integer Programming (MIP) problem
-    that can then be solved (provably) optimally using a MIP solver. This is integrated
-    with CPLEX, a fast commercial solver sold by IBM. There are free and open source MIP
-    solvers, but they come with substantial slowdowns (often many orders of magnitude).
-    By representing the original circuit as a Directed Acyclic Graph (DAG), this class
-    can find the optimal wire cuts in the circuit.
-    """
-
-    @deprecate_func(
-        removal_timeline="no sooner than CKT v0.8.0",
-        since="0.7.0",
-        package_name="circuit-knitting-toolbox",
-        additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-    )
-    def __init__(
-        self,
-        n_vertices: int,
-        edges: Sequence[tuple[int]],
-        vertex_ids: dict[str, int],
-        id_vertices: dict[int, str],
-        num_subcircuit: int,
-        max_subcircuit_width: int,
-        max_subcircuit_cuts: int,
-        max_subcircuit_size: int,
-        num_qubits: int,
-        max_cuts: int,
-    ):
-        """
-        Initialize member variables.
-
-        Args:
-            n_vertices: The number of vertices in the circuit DAG
-            edges: The list of edges of the circuit DAG
-            n_edges: The number of edges
-            vertex_ids: Dictionary mapping vertices (i.e. two qubit gates) to the vertex
-                id (i.e. a number)
-            id_vertices: The inverse dictionary of vertex_ids, which has keys of vertex ids
-                and values of the vertices
-            num_subcircuit: The number of subcircuits
-            max_subcircuit_width: Maximum number of qubits per subcircuit
-            max_subcircuit_cuts: Maximum number of cuts in each subcircuit
-            max_subcircuit_size: Maximum number of gates in a subcircuit
-            num_qubits: The number of qubits in the circuit
-            max_cuts: The maximum total number of cuts
-
-        Returns:
-            None
-        """
-        self.check_graph(n_vertices, edges)
-        self.n_vertices = n_vertices
-        self.edges = edges
-        self.n_edges = len(edges)
-        self.vertex_ids = vertex_ids
-        self.id_vertices = id_vertices
-        self.num_subcircuit = num_subcircuit
-        self.max_subcircuit_width = max_subcircuit_width
-        self.max_subcircuit_cuts = max_subcircuit_cuts
-        self.max_subcircuit_size = max_subcircuit_size
-        self.num_qubits = num_qubits
-        self.max_cuts = max_cuts
-
-        self.subcircuit_counter: dict[int, dict[str, Any]] = {}
-
-        """
-        Count the number of input qubits directly connected to each node
-        """
-        self.vertex_weight = {}
-        for node in self.vertex_ids:
-            qargs = node.split(" ")
-            num_in_qubits = 0
-            for qarg in qargs:
-                if int(qarg.split("]")[1]) == 0:
-                    num_in_qubits += 1
-            self.vertex_weight[node] = num_in_qubits
-
-        try:
-            from docplex.mp.model import Model
-        except ModuleNotFoundError as ex:  # pragma: no cover
-            raise ModuleNotFoundError(
-                "DOcplex is not installed.  For automatic cut-finding to work, both "
-                "DOcplex and cplex must be available."
-            ) from ex
-
-        self.model = Model("docplex_cutter")
-        self.model.log_output = False
-        self._add_variables()
-        self._add_constraints()
-
-    def _add_variables(self) -> None:
-        """Add the necessary variables to the CPLEX model."""
-        """
-        Indicate if a vertex is in some subcircuit
-        """
-        self.vertex_var = []
-        for i in range(self.num_subcircuit):
-            subcircuit_y = []
-            for j in range(self.n_vertices):
-                varName = "bin_sc_" + str(i) + "_vx_" + str(j)
-                loc_var = self.model.binary_var(name=varName)
-                subcircuit_y.append(loc_var)
-            self.vertex_var.append(subcircuit_y)
-
-        """
-        Indicate if an edge has one and only one vertex in some subcircuit
-        """
-        self.edge_var = []
-        for i in range(self.num_subcircuit):
-            subcircuit_x = []
-            for j in range(self.n_edges):
-                varName = "bin_sc_" + str(i) + "_edg_" + str(j)
-                loc_var = self.model.binary_var(name=varName)
-                subcircuit_x.append(loc_var)
-            self.edge_var.append(subcircuit_x)
-
-        """
-        Total number of cuts
-        add 0.1 for numerical stability
-        """
-        self.num_cuts = self.model.integer_var(
-            lb=0, ub=self.max_cuts + 0.1, name="num_cuts"
-        )
-
-        for subcircuit in range(self.num_subcircuit):
-            self.subcircuit_counter[subcircuit] = {}
-
-            self.subcircuit_counter[subcircuit]["original_input"] = (
-                self.model.integer_var(
-                    lb=0,
-                    ub=self.max_subcircuit_width,
-                    name="original_input_%d" % subcircuit,
-                )
-            )
-            self.subcircuit_counter[subcircuit]["rho"] = self.model.integer_var(
-                lb=0, ub=self.max_subcircuit_width, name="rho_%d" % subcircuit
-            )
-            self.subcircuit_counter[subcircuit]["O"] = self.model.integer_var(
-                lb=0, ub=self.max_subcircuit_width, name="O_%d" % subcircuit
-            )
-            self.subcircuit_counter[subcircuit]["d"] = self.model.integer_var(
-                lb=0.1, ub=self.max_subcircuit_width, name="d_%d" % subcircuit
-            )
-            if self.max_subcircuit_size is not None:
-                self.subcircuit_counter[subcircuit]["size"] = self.model.integer_var(
-                    lb=0.1, ub=self.max_subcircuit_size, name="size_%d" % subcircuit
-                )
-            if self.max_subcircuit_cuts is not None:
-                self.subcircuit_counter[subcircuit]["num_cuts"] = (
-                    self.model.integer_var(
-                        lb=0.1,
-                        ub=self.max_subcircuit_cuts,
-                        name="num_cuts_%d" % subcircuit,
-                    )
-                )
-
-            self.subcircuit_counter[subcircuit]["rho_qubit_product"] = []
-            self.subcircuit_counter[subcircuit]["O_qubit_product"] = []
-            for i in range(self.n_edges):
-                edge_var_downstream_vertex_var_product = self.model.binary_var(
-                    name="bin_edge_var_downstream_vertex_var_product_%d_%d"
-                    % (subcircuit, i)
-                )
-                self.subcircuit_counter[subcircuit]["rho_qubit_product"].append(
-                    edge_var_downstream_vertex_var_product
-                )
-                edge_var_upstream_vertex_var_product = self.model.binary_var(
-                    name="bin_edge_var_upstream_vertex_var_product_%d_%d"
-                    % (subcircuit, i)
-                )
-                self.subcircuit_counter[subcircuit]["O_qubit_product"].append(
-                    edge_var_upstream_vertex_var_product
-                )
-
-            if subcircuit > 0:
-                lb = 0
-                ub = self.num_qubits + 2 * self.max_cuts + 1
-                self.subcircuit_counter[subcircuit]["build_cost_exponent"] = (
-                    self.model.integer_var(
-                        lb=lb, ub=ub, name="build_cost_exponent_%d" % subcircuit
-                    )
-                )
-
-    def _add_constraints(self) -> None:
-        """Add all contraints and objectives to MIP model."""
-        """
-        each vertex in exactly one subcircuit
-        """
-        for v in range(self.n_vertices):
-            ctName = "cons_vertex_" + str(v)
-            self.model.add_constraint(
-                self.model.sum(
-                    self.vertex_var[i][v] for i in range(self.num_subcircuit)
-                )
-                == 1,
-                ctname=ctName,
-            )
-
-        """
-        edge_var=1 indicates one and only one vertex of an edge is in subcircuit
-        edge_var[subcircuit][edge] = vertex_var[subcircuit][u] XOR vertex_var[subcircuit][v]
-        """
-        for i in range(self.num_subcircuit):
-            for e in range(self.n_edges):
-                if len(self.edges[e]) != 2:
-                    raise ValueError(
-                        "Edges should be length 2 sequences: {self.edges[e]}"
-                    )
-                u = self.edges[e][0]
-                v = self.edges[e][-1]
-                u_vertex_var = self.vertex_var[i][u]
-                v_vertex_var = self.vertex_var[i][v]
-                ctName = "cons_edge_" + str(e)
-                self.model.add_constraint(
-                    self.edge_var[i][e] - u_vertex_var - v_vertex_var <= 0,
-                    ctname=ctName + "_1",
-                )
-                self.model.add_constraint(
-                    self.edge_var[i][e] - u_vertex_var + v_vertex_var >= 0,
-                    ctname=ctName + "_2",
-                )
-                self.model.add_constraint(
-                    self.edge_var[i][e] - v_vertex_var + u_vertex_var >= 0,
-                    ctname=ctName + "_3",
-                )
-                self.model.add_constraint(
-                    self.edge_var[i][e] + u_vertex_var + v_vertex_var <= 2,
-                    ctname=ctName + "_4",
-                )
-
-        """
-        Symmetry-breaking constraints
-        Force small-numbered vertices into small-numbered subcircuits:
-            v0: in subcircuit 0
-            v1: in subcircuit_0 or subcircuit_1
-            v2: in subcircuit_0 or subcircuit_1 or subcircuit_2
-            ...
-        """
-        for vertex in range(self.num_subcircuit):
-            ctName = "cons_symm_" + str(vertex)
-            self.model.add_constraint(
-                self.model.sum(
-                    self.vertex_var[subcircuit][vertex]
-                    for subcircuit in range(vertex + 1)
-                )
-                == 1,
-                ctname=ctName,
-            )
-
-        """
-        Compute number of cuts
-        """
-        self.model.add_constraint(
-            self.num_cuts
-            == self.model.sum(
-                [
-                    self.edge_var[subcircuit][i]
-                    for i in range(self.n_edges)
-                    for subcircuit in range(self.num_subcircuit)
-                ]
-            )
-            / 2
-        )
-
-        num_effective_qubits = []
-        for subcircuit in range(self.num_subcircuit):
-            """
-            Compute number of different types of qubit in a subcircuit
-            """
-            self.model.add_constraint(
-                self.subcircuit_counter[subcircuit]["original_input"]
-                - self.model.sum(
-                    self.vertex_weight[self.id_vertices[i]]
-                    * self.vertex_var[subcircuit][i]
-                    for i in range(self.n_vertices)
-                )
-                == 0,
-                ctname="cons_subcircuit_input_%d" % subcircuit,
-            )
-
-            for i in range(self.n_edges):
-                if len(self.edges[i]) != 2:
-                    raise ValueError(
-                        "Edges should be length 2 sequences: {self.edges[i]}"
-                    )
-                self.model.add_constraint(
-                    self.subcircuit_counter[subcircuit]["rho_qubit_product"][i]
-                    == self.edge_var[subcircuit][i]
-                    & self.vertex_var[subcircuit][self.edges[i][-1]],
-                    ctname="cons_edge_var_downstream_vertex_var_%d_%d"
-                    % (subcircuit, i),
-                )
-
-            self.model.add_constraint(
-                self.subcircuit_counter[subcircuit]["rho"]
-                - self.model.sum(
-                    self.subcircuit_counter[subcircuit]["rho_qubit_product"]
-                )
-                == 0,
-                ctname="cons_subcircuit_rho_qubits_%d" % subcircuit,
-            )
-
-            for i in range(self.n_edges):
-                self.model.add_constraint(
-                    self.subcircuit_counter[subcircuit]["O_qubit_product"][i]
-                    == self.edge_var[subcircuit][i]
-                    & self.vertex_var[subcircuit][self.edges[i][0]],
-                    ctname="cons_edge_var_upstream_vertex_var_%d_%d" % (subcircuit, i),
-                )
-            self.model.add_constraint(
-                self.subcircuit_counter[subcircuit]["O"]
-                - self.model.sum(self.subcircuit_counter[subcircuit]["O_qubit_product"])
-                == 0,
-                ctname="cons_subcircuit_O_qubits_%d" % subcircuit,
-            )
-
-            self.model.add_constraint(
-                self.subcircuit_counter[subcircuit]["d"]
-                - self.subcircuit_counter[subcircuit]["original_input"]
-                - self.subcircuit_counter[subcircuit]["rho"]
-                == 0,
-                ctname="cons_subcircuit_d_qubits_%d" % subcircuit,
-            )
-
-            if self.max_subcircuit_cuts is not None:
-                self.model.add_constraint(
-                    self.subcircuit_counter[subcircuit]["num_cuts"]
-                    - self.subcircuit_counter[subcircuit]["rho"]
-                    - self.subcircuit_counter[subcircuit]["O"]
-                    == 0,
-                    ctname="cons_subcircuit_num_cuts_%d" % subcircuit,
-                )
-
-            if self.max_subcircuit_size is not None:
-                self.model.add_constraint(
-                    self.subcircuit_counter[subcircuit]["size"]
-                    - self.model.sum(
-                        [self.vertex_var[subcircuit][v] for v in range(self.n_vertices)]
-                    )
-                    == 0,
-                    ctname="cons_subcircuit_size_%d" % subcircuit,
-                )
-
-            num_effective_qubits.append(
-                self.subcircuit_counter[subcircuit]["d"]
-                - self.subcircuit_counter[subcircuit]["O"]
-            )
-
-            """
-            Compute the classical postprocessing cost
-            """
-            if subcircuit > 0:
-                ptx, ptf = self.pwl_exp(
-                    lb=int(
-                        self.subcircuit_counter[subcircuit]["build_cost_exponent"].lb
-                    ),
-                    ub=int(
-                        self.subcircuit_counter[subcircuit]["build_cost_exponent"].ub
-                    ),
-                    base=2,
-                    coefficient=1,
-                    integer_only=True,
-                )
-                self.model.add_constraint(
-                    self.subcircuit_counter[subcircuit]["build_cost_exponent"]
-                    - self.model.sum(num_effective_qubits)
-                    - 2 * self.num_cuts
-                    == 0,
-                    ctname="cons_build_cost_exponent_%d" % subcircuit,
-                )
-                # TODO: add PWL objective in CPLEX
-                # self.model.setPWLObj(self.subcircuit_counter[subcircuit]['build_cost_exponent'], ptx, ptf)
-
-        # self.model.setObjective(self.num_cuts,gp.GRB.MINIMIZE)
-        self.model.set_objective("min", self.num_cuts)
-
-    def pwl_exp(
-        self, lb: int, ub: int, base: int, coefficient: int, integer_only: bool
-    ) -> tuple[list[int], list[int]]:
-        r"""
-        Approximate a nonlinear exponential function via a piecewise linear function.
-
-        Args:
-            lb: Lower bound
-            ub: Upper bound
-            base: The base of the input exponential
-            coefficient: The coefficient of the original exponential
-            integer_only: Whether the input x's are only integers
-
-        Returns:
-            A tuple containing the :math:`x`\ s and :math:`f(x)`\ s of the piecewise approximation
-        """
-        # Piecewise linear approximation of coefficient*base**x
-        ptx = []
-        ptf = []
-
-        x_range = range(lb, ub + 1) if integer_only else list(np.linspace(lb, ub, 200))
-        # print('x_range : {}, integer_only : {}'.format(x_range,integer_only))
-        for x in x_range:
-            y = coefficient * base**x
-            ptx.append(x)
-            ptf.append(y)
-        return ptx, ptf
-
-    def check_graph(self, n_vertices: int, edges: Sequence[tuple[int]]) -> None:
-        """
-        Ensure circuit DAG is viable.
-
-        This means that there are no oversized edges, that all edges are from viable nodes,
-        and that the graph is otherwise a valid graph.
-
-        Args:
-            n_vertices: The number of vertices
-            edges: The edge list
-
-        Returns:
-            None
-
-        Raises:
-            ValueError: The graph is invalid
-        """
-        # 1. edges must include all vertices
-        # 2. all u,v must be ordered and smaller than n_vertices
-        vertices = set([i for (i, _) in edges])  # type: ignore
-        vertices |= set([i for (_, i) in edges])  # type: ignore
-        assert vertices == set(range(n_vertices))
-        for edge in edges:
-            if len(edge) != 2:
-                raise ValueError("Edges should be length 2 sequences: {edge}")
-            u = edge[0]
-            v = edge[-1]
-            if u > v:
-                raise ValueError(f"Edge u ({u}) cannot be greater than edge v ({v})")
-            if u > n_vertices:
-                raise ValueError(
-                    f"Edge u ({u}) cannot be greater than number of vertices ({n_vertices})"
-                )
-
-    def solve(self, min_postprocessing_cost: float) -> bool:
-        """
-        Solve the MIP model.
-
-        Args:
-            min_post_processing_cost: The predicted minimum post-processing cost,
-                often is inf
-
-        Returns:
-            Flag denoting whether or not the model found a solution
-        """
-        # print('solving for %d subcircuits'%self.num_subcircuit)
-        # print('model has %d variables, %d linear constraints,%d quadratic constraints, %d general constraints'
-        # % (self.model.NumVars,self.model.NumConstrs, self.model.NumQConstrs, self.model.NumGenConstrs))
-        from docplex.mp.utils import DOcplexException
-
-        print(
-            "Exporting as a LP file to let you check the model that will be solved : ",
-            min_postprocessing_cost,
-            str(type(min_postprocessing_cost)),
-        )
-        try:
-            self.model.export_as_lp(path="./docplex_cutter.lp")
-        except RuntimeError:
-            print(
-                "The LP file export has failed.  This is known to happen sometimes "
-                "when cplex is not installed.  Now attempting to continue anyway."
-            )
-        try:
-            self.model.set_time_limit(300)
-            if min_postprocessing_cost != float("inf"):
-                self.model.parameters.mip.tolerances.uppercutoff(
-                    min_postprocessing_cost
-                )
-            self.model.solve(log_output=True)
-
-        except DOcplexException as e:
-            print("Caught: " + e.message)
-            raise e
-
-        if self.model._has_solution:
-            my_solve_details = self.model.solve_details
-            self.subcircuits = []
-            self.optimal = my_solve_details.status == "optimal"
-            self.runtime = my_solve_details.time
-            self.node_count = my_solve_details.nb_nodes_processed
-            self.mip_gap = my_solve_details.mip_relative_gap
-            self.objective = self.model.objective_value
-
-            for i in range(self.num_subcircuit):
-                subcircuit = []
-                for j in range(self.n_vertices):
-                    if abs(self.vertex_var[i][j].solution_value) > 1e-4:
-                        subcircuit.append(self.id_vertices[j])
-                self.subcircuits.append(subcircuit)
-            assert (
-                sum([len(subcircuit) for subcircuit in self.subcircuits])
-                == self.n_vertices
-            )
-
-            cut_edges_idx = []
-            cut_edges = []
-            for i in range(self.num_subcircuit):
-                for j in range(self.n_edges):
-                    if (
-                        abs(self.edge_var[i][j].solution_value) > 1e-4
-                        and j not in cut_edges_idx
-                    ):
-                        cut_edges_idx.append(j)
-                        if len(self.edges[j]) != 2:
-                            raise ValueError("Edges should be length-2 sequences.")
-                        u = self.edges[j][0]
-                        v = self.edges[j][-1]
-                        cut_edges.append((self.id_vertices[u], self.id_vertices[v]))
-            self.cut_edges = cut_edges
-            return True
-        else:
-            return False
diff --git a/circuit_knitting/cutting/cutqc/wire_cutting.py b/circuit_knitting/cutting/cutqc/wire_cutting.py
deleted file mode 100644
index 02025f844..000000000
--- a/circuit_knitting/cutting/cutqc/wire_cutting.py
+++ /dev/null
@@ -1,1067 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""Functions for conducting the wire cutting on quantum circuits."""
-
-from __future__ import annotations
-
-from typing import Sequence, Any, Dict, cast, no_type_check
-
-import numpy as np
-from qiskit import QuantumCircuit, QuantumRegister
-from qiskit.circuit import Qubit
-from qiskit.dagcircuit import DAGCircuit, DAGOpNode
-from qiskit.converters import circuit_to_dag, dag_to_circuit
-from qiskit_ibm_runtime import Options, QiskitRuntimeService
-from qiskit.utils.deprecation import deprecate_func
-
-from .wire_cutting_evaluation import run_subcircuit_instances
-from .wire_cutting_post_processing import generate_summation_terms, build
-from .wire_cutting_verification import generate_reconstructed_output
-from .dynamic_definition import (
-    dd_build,
-    get_reconstruction_qubit_order,
-    read_dd_bins,
-)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def cut_circuit_wires(
-    circuit: QuantumCircuit,
-    method: str,
-    subcircuit_vertices: Sequence[Sequence[int]] | None = None,
-    max_subcircuit_width: int | None = None,
-    max_subcircuit_cuts: int | None = None,
-    max_subcircuit_size: int | None = None,
-    max_cuts: int | None = None,
-    num_subcircuits: Sequence[int] | None = None,
-    verbose: bool = True,
-) -> dict[str, Any]:
-    """
-    Decompose the circuit into a collection of subcircuits.
-
-    Args:
-        method: Whether to have the cuts be 'automatically' found, in a
-            provably optimal way, or whether to 'manually' specify the cuts
-        subcircuit_vertices: The vertices to be used in the subcircuits. Note
-            that these are not the indices of the qubits, but the nodes in the circuit DAG
-        max_subcircuit_width: Max number of qubits in each subcircuit
-        max_cuts: Max total number of cuts allowed
-        num_subcircuits: List of number of subcircuits to try
-        max_subcircuit_cuts: Max number of cuts for a subcircuit
-        max_subcircuit_size: Max number of gates in a subcircuit
-        verbose: Flag for printing output of cutting
-
-    Returns:
-        A dictionary containing information on the cuts, including the subcircuits
-        themselves (key: 'subcircuits')
-
-    Raises:
-        ValueError: The input method does not match the other provided arguments
-    """
-    cuts = {}
-    if method == "automatic":
-        if max_subcircuit_width is None:
-            raise ValueError(
-                "The max_subcircuit_width argument must be set if using automatic cut-finding."
-            )
-        cuts = find_wire_cuts(
-            circuit=circuit,
-            max_subcircuit_width=max_subcircuit_width,
-            max_cuts=max_cuts,
-            num_subcircuits=num_subcircuits,
-            max_subcircuit_cuts=max_subcircuit_cuts,
-            max_subcircuit_size=max_subcircuit_size,
-            verbose=verbose,
-        )
-    elif method == "manual":
-        if subcircuit_vertices is None:
-            raise ValueError(
-                "The subcircuit_vertices argument must be set if manually specifying cuts."
-            )
-        cuts = cut_circuit_wire(
-            circuit=circuit, subcircuit_vertices=subcircuit_vertices, verbose=verbose
-        )
-    else:
-        ValueError(
-            'The method argument for the decompose method should be either "automatic" or "manual".'
-        )
-
-    return cuts
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def evaluate_subcircuits(
-    cuts: dict[str, Any],
-    service: QiskitRuntimeService | None = None,
-    backend_names: str | Sequence[str] | None = None,
-    options: Options | Sequence[Options] | None = None,
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Evaluate the subcircuits.
-
-    Args:
-        cuts: The results of cutting
-        service: A service for connecting to Qiskit Runtime Service
-        options: Options to use on each backend
-        backend_names: The name(s) of the backend(s) to be used
-
-    Returns:
-        The dictionary containing the results from running each of the subcircuits
-    """
-    # Put backend_names and options in lists to ensure it is unambiguous how to sync them
-    backends_list: Sequence[str] = []
-    options_list: Sequence[Options] = []
-    if backend_names is None or isinstance(backend_names, str):
-        if isinstance(options, Options):
-            options_list = [options]
-        elif isinstance(options, Sequence) and (len(options) != 1):
-            options_list = [options[0]]
-        if isinstance(backend_names, str):
-            backends_list = [backend_names]
-    else:
-        backends_list = backend_names
-        if isinstance(options, Options):
-            options_list = [options] * len(backends_list)
-        elif options is None:
-            options_list = [None] * len(backends_list)
-        else:
-            options_list = options
-
-    if backend_names:
-        if len(backends_list) != len(options_list):
-            raise AttributeError(
-                f"The list of backend names is length ({len(backends_list)}), "
-                f"but the list of options is length ({len(options_list)}). "
-                "It is ambiguous how these options should be applied."
-            )
-
-    _, _, subcircuit_instances = _generate_metadata(cuts)
-
-    subcircuit_instance_probabilities = _run_subcircuits(
-        cuts,
-        subcircuit_instances,
-        service=service,
-        backend_names=backends_list,
-        options=options_list,
-    )
-
-    return subcircuit_instance_probabilities
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def reconstruct_full_distribution(
-    circuit: QuantumCircuit,
-    subcircuit_instance_probabilities: dict[int, dict[int, np.ndarray]],
-    cuts: dict[str, Any],
-    num_threads: int = 1,
-) -> np.ndarray:
-    """
-    Reconstruct the full probabilities from the subcircuit evaluations.
-
-    Args:
-        circuit: The original full circuit
-        subcircuit_instance_probabilities: The probability vectors from each
-            of the subcircuit instances, as output by the _run_subcircuits function
-        num_threads: The number of threads to use to parallelize the recomposing
-
-    Returns:
-        The reconstructed probability vector
-    """
-    summation_terms, subcircuit_entries, _ = _generate_metadata(cuts)
-
-    subcircuit_entry_probabilities = _attribute_shots(
-        subcircuit_entries, subcircuit_instance_probabilities
-    )
-
-    unordered_probability, smart_order, overhead = build(
-        summation_terms=summation_terms,
-        subcircuit_entry_probs=subcircuit_entry_probabilities,
-        num_cuts=cuts["num_cuts"],
-        num_threads=num_threads,
-    )
-
-    reconstructed_probability = generate_reconstructed_output(
-        circuit,
-        cuts["subcircuits"],
-        unordered_probability,
-        smart_order,
-        cuts["complete_path_map"],
-    )
-
-    return reconstructed_probability
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def create_dd_bin(
-    subcircuit_instance_probabilities: dict[int, dict[int, np.ndarray]],
-    cuts: dict[str, Any],
-    mem_limit: int,
-    recursion_depth: int,
-    num_threads: int = 1,
-) -> dict[int, Any]:
-    """
-    Create a bin for Dynamic Definition.
-
-    Args:
-        subcircuit_instance_probabilities: The probability vectors from each
-            of the subcircuit instances, as output by the _run_subcircuits function
-        cuts: The results of cutting
-        mem_limit: maximum system memory
-        recursion_depth: the number of recursive call for Dynamic Definition
-        num_threads: The number of threads to use to parallelize the recomposing
-
-    Returns:
-        The bin for dynamic definition
-    """
-    summation_terms, subcircuit_entries, subcircuit_instances = _generate_metadata(cuts)
-
-    subcircuit_entry_probabilities = _attribute_shots(
-        subcircuit_entries, subcircuit_instance_probabilities
-    )
-
-    return dd_build(
-        summation_terms=summation_terms,
-        subcircuit_entry_probs=subcircuit_entry_probabilities,
-        num_cuts=cuts["num_cuts"],
-        mem_limit=mem_limit,
-        recursion_depth=recursion_depth,
-        counter=cuts["counter"],
-        subcircuit_instances=subcircuit_instances,
-        num_threads=num_threads,
-    )
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def reconstruct_dd_full_distribution(
-    circuit: QuantumCircuit,
-    cuts: dict[str, Any],
-    dd_bins: dict[int, Any],
-) -> np.ndarray:
-    """
-    Reconstruct the full probabilities from bins of dynamic definition.
-
-    Args:
-        circuit: The original full circuit
-        cuts: The results of cutting
-        dd_bins: The bin for Dynamic Definition
-
-    Returns:
-        The reconstructed probability vector
-    """
-    subcircuit_out_qubits = get_reconstruction_qubit_order(
-        full_circuit=circuit,
-        complete_path_map=cuts["complete_path_map"],
-        subcircuits=cuts["subcircuits"],
-    )
-    reconstructed_prob = read_dd_bins(
-        subcircuit_out_qubits=subcircuit_out_qubits, dd_bins=dd_bins
-    )
-
-    return reconstructed_prob
-
-
-def _generate_metadata(cuts: dict[str, Any]) -> tuple[
-    list[dict[int, int]],
-    dict[int, dict[tuple[str, str], tuple[int, Sequence[tuple[int, int]]]]],
-    dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-]:
-    """
-    Generate metadata used to execute subcircuits and reconstruct probabilities of original circuit.
-
-    Args:
-        cuts: Results from the cutting step
-
-    Returns:
-        Information about the 4^(num cuts) summation terms used to reconstruct original
-        probabilities, a dictionary with information on each of the subcircuits, and a dictionary
-        containing indexes for each of the subcircuits
-    """
-    (
-        summation_terms,
-        subcircuit_entries,
-        subcircuit_instances,
-    ) = generate_summation_terms(
-        subcircuits=cuts["subcircuits"],
-        complete_path_map=cuts["complete_path_map"],
-        num_cuts=cuts["num_cuts"],
-    )
-    return summation_terms, subcircuit_entries, subcircuit_instances
-
-
-def _run_subcircuits(
-    cuts: dict[str, Any],
-    subcircuit_instances: dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-    service: QiskitRuntimeService | None = None,
-    backend_names: Sequence[str] | None = None,
-    options: Sequence[Options] | None = None,
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Execute all the subcircuit instances.
-
-    task['subcircuit_instance_probs'][subcircuit_idx][subcircuit_instance_idx] = measured prob
-
-    Args:
-        cuts: Results from the cutting step
-        subcircuit_instances: The dictionary containing the index information for each
-            of the subcircuit instances
-        service: The arguments for the runtime service
-        backend_names: The backend(s) used to run the subcircuits
-        options: Options for the runtime execution of subcircuits
-
-    Returns:
-        The resulting probabilities from each of the subcircuit instances
-    """
-    subcircuit_instance_probs = run_subcircuit_instances(
-        subcircuits=cuts["subcircuits"],
-        subcircuit_instances=subcircuit_instances,
-        service=service,
-        backend_names=backend_names,
-        options=options,
-    )
-
-    return subcircuit_instance_probs
-
-
-def _attribute_shots(
-    subcircuit_entries: dict[
-        int, dict[tuple[str, str], tuple[int, Sequence[tuple[int, int]]]]
-    ],
-    subcircuit_instance_probs: dict[int, dict[int, np.ndarray]],
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Attribute the shots into respective subcircuit entries.
-
-    task['subcircuit_entry_probs'][subcircuit_idx][subcircuit_entry_idx] = prob
-
-    Args:
-        subcircuit_entries: Dictionary containing information about each of the
-            subcircuit instances
-        subcircuit_instance_probs: The probability vectors from each of the subcircuit
-            instances, as output by the _run_subcircuits function
-
-    Returns:
-        A dictionary containing the probability results to each of the appropriate subcircuits
-
-    Raises:
-        ValueError: A kronecker term is not size two
-        ValueError: There are no subcircuit probs provided
-    """
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]] = {}
-    for subcircuit_idx in subcircuit_entries:
-        subcircuit_entry_probs[subcircuit_idx] = {}
-        for label in subcircuit_entries[subcircuit_idx]:
-            subcircuit_entry_idx, kronecker_term = subcircuit_entries[subcircuit_idx][
-                label
-            ]
-            subcircuit_entry_prob: np.ndarray | None = None
-            for coefficient, subcircuit_instance_idx in kronecker_term:
-                if subcircuit_entry_prob is None:
-                    subcircuit_entry_prob = (
-                        coefficient
-                        * subcircuit_instance_probs[subcircuit_idx][
-                            subcircuit_instance_idx
-                        ]
-                    )
-                else:
-                    subcircuit_entry_prob += (
-                        coefficient
-                        * subcircuit_instance_probs[subcircuit_idx][
-                            subcircuit_instance_idx
-                        ]
-                    )
-
-            if subcircuit_entry_prob is None:
-                raise ValueError(
-                    "Something unexpected happened during shot attribution."
-                )
-            subcircuit_entry_probs[subcircuit_idx][
-                subcircuit_entry_idx
-            ] = subcircuit_entry_prob
-
-    return subcircuit_entry_probs
-
-
-@no_type_check
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def find_wire_cuts(
-    circuit: QuantumCircuit,
-    max_subcircuit_width: int,
-    max_cuts: int | None,
-    num_subcircuits: Sequence[int] | None,
-    max_subcircuit_cuts: int | None,
-    max_subcircuit_size: int | None,
-    verbose: bool,
-) -> dict[str, Any]:
-    """
-    Find optimal cuts for the wires.
-
-    Will print if the model cannot find a solution at all, and will print whether
-    the found solution is optimal or not.
-
-    Args:
-        circuit: Original quantum circuit to be cut into subcircuits
-        max_subcircuit_width: Max number of qubits in each subcircuit
-        max_cuts: Max total number of cuts allowed
-        num_subcircuits: List of number of subcircuits to try
-        max_subcircuit_cuts: Max number of cuts for a subcircuit
-        max_subcircuit_size: The maximum number of two qubit gates in each
-            subcircuit
-        verbose: Whether to print information about the cut-finding or not
-
-    Returns:
-        The solution found for the cuts
-    """
-    stripped_circ = _circuit_stripping(circuit=circuit)
-    n_vertices, edges, vertex_ids, id_vertices = _read_circuit(circuit=stripped_circ)
-    num_qubits = circuit.num_qubits
-    cut_solution = {}
-    min_cost = float("inf")
-
-    best_mip_model = None
-    for num_subcircuit in num_subcircuits:
-        if (
-            num_subcircuit * max_subcircuit_width - (num_subcircuit - 1) < num_qubits
-            or num_subcircuit > num_qubits
-            or max_cuts + 1 < num_subcircuit
-        ):
-            if verbose:
-                print("%d subcircuits : IMPOSSIBLE" % (num_subcircuit))
-            continue
-        kwargs = dict(  # pylint: disable=use-dict-literal
-            n_vertices=n_vertices,
-            edges=edges,
-            vertex_ids=vertex_ids,
-            id_vertices=id_vertices,
-            num_subcircuit=num_subcircuit,
-            max_subcircuit_width=max_subcircuit_width,
-            max_subcircuit_cuts=max_subcircuit_cuts,
-            max_subcircuit_size=max_subcircuit_size,
-            num_qubits=num_qubits,
-            max_cuts=max_cuts,
-        )
-
-        from .mip_model import MIPModel
-
-        mip_model = MIPModel(**kwargs)
-        feasible = mip_model.solve(min_postprocessing_cost=min_cost)
-        if not feasible:
-            if verbose:
-                print("%d subcircuits : NO SOLUTIONS" % (num_subcircuit))
-            continue
-        else:
-            positions = _cuts_parser(mip_model.cut_edges, circuit)
-            subcircuits, complete_path_map = _subcircuits_parser(
-                subcircuit_gates=mip_model.subcircuits, circuit=circuit
-            )
-            O_rho_pairs = _get_pairs(complete_path_map=complete_path_map)
-            counter = _get_counter(subcircuits=subcircuits, O_rho_pairs=O_rho_pairs)
-
-            classical_cost = _cost_estimate(counter=counter)
-            cost = classical_cost
-
-            if cost < min_cost:
-                min_cost = cost
-                best_mip_model = mip_model
-                cut_solution = {
-                    "max_subcircuit_width": max_subcircuit_width,
-                    "subcircuits": subcircuits,
-                    "complete_path_map": complete_path_map,
-                    "num_cuts": len(positions),
-                    "counter": counter,
-                    "classical_cost": classical_cost,
-                }
-    if verbose and len(cut_solution) > 0:
-        print("-" * 20)
-        classical_cost: float = float(cut_solution["classical_cost"])
-        # We can remove typing.Dict from this cast statement when py38 is deprecated.
-        # https://bugs.python.org/issue45117
-        counter = cast(Dict[int, Dict[str, int]], cut_solution["counter"])
-        subcircuits: Sequence[Any] = cut_solution["subcircuits"]
-        num_cuts: int = cut_solution["num_cuts"]
-        _print_cutter_result(
-            num_cuts=num_cuts,
-            subcircuits=subcircuits,
-            counter=counter,
-            classical_cost=classical_cost,
-        )
-
-        if best_mip_model is None:
-            raise ValueError(
-                "Something went wrong during cut-finding. The best MIP model object was never instantiated."
-            )
-        print("Model objective value = %.2e" % (best_mip_model.objective), flush=True)  # type: ignore
-        print("MIP runtime:", best_mip_model.runtime, flush=True)
-
-        if best_mip_model.optimal:
-            print("OPTIMAL, MIP gap =", best_mip_model.mip_gap, flush=True)
-        else:
-            print("NOT OPTIMAL, MIP gap =", best_mip_model.mip_gap, flush=True)
-        print("-" * 20, flush=True)
-    return cut_solution
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def cut_circuit_wire(
-    circuit: QuantumCircuit, subcircuit_vertices: Sequence[Sequence[int]], verbose: bool
-) -> dict[str, Any]:
-    """
-    Perform the provided cuts.
-
-    Used when cut locations are chosen manually.
-
-    Args:
-        circuit: Original quantum circuit to be cut into subcircuits
-        subcircuit_vertices: The list of vertices to apply the cuts to
-        verbose: Whether to print the details of cutting or not
-
-    Returns:
-        The solution calculated from the provided cuts
-    """
-    stripped_circ = _circuit_stripping(circuit=circuit)
-    n_vertices, edges, vertex_ids, id_vertices = _read_circuit(circuit=stripped_circ)
-
-    subcircuit_list = []
-    for vertices in subcircuit_vertices:
-        subcircuit = []
-        for vertex in vertices:
-            subcircuit.append(id_vertices[vertex])
-        subcircuit_list.append(subcircuit)
-    if sum([len(subcircuit) for subcircuit in subcircuit_list]) != n_vertices:
-        raise ValueError("Not all gates are assigned into subcircuits")
-
-    subcircuit_object = _subcircuits_parser(
-        subcircuit_gates=subcircuit_list, circuit=circuit
-    )
-    if len(subcircuit_object) != 2:
-        raise ValueError("subcircuit_object should contain exactly two elements.")
-    subcircuits = subcircuit_object[0]
-    complete_path_map = subcircuit_object[-1]
-
-    O_rho_pairs = _get_pairs(complete_path_map=complete_path_map)
-    counter = _get_counter(subcircuits=subcircuits, O_rho_pairs=O_rho_pairs)
-    classical_cost = _cost_estimate(counter=counter)
-    max_subcircuit_width = max([subcirc.width() for subcirc in subcircuits])  # type: ignore
-
-    cut_solution = {
-        "max_subcircuit_width": max_subcircuit_width,
-        "subcircuits": subcircuits,
-        "complete_path_map": complete_path_map,
-        "num_cuts": len(O_rho_pairs),
-        "counter": counter,
-        "classical_cost": classical_cost,
-    }
-
-    if verbose:
-        print("-" * 20)
-        _print_cutter_result(
-            num_cuts=cut_solution["num_cuts"],
-            subcircuits=cut_solution["subcircuits"],
-            counter=cut_solution["counter"],
-            classical_cost=cut_solution["classical_cost"],
-        )
-        print("-" * 20)
-    return cut_solution
-
-
-def _print_cutter_result(
-    num_cuts: int,
-    subcircuits: Sequence[QuantumCircuit],
-    counter: dict[int, dict[str, int]],
-    classical_cost: float,
-) -> None:
-    """
-    Pretty print the results.
-
-    Args:
-        num_cuts: The number of cuts
-        subcircuits: The list of subcircuits
-        counter: The dictionary containing all meta information regarding
-            each of the subcircuits
-        classical_cost: The estimated processing cost
-
-    Returns:
-        None
-    """
-    print(f"num_cuts = {num_cuts}")
-    for subcircuit_idx, subcircuit in enumerate(subcircuits):
-        print("subcircuit %d" % subcircuit_idx)
-        print(
-            "\u03C1 qubits = %d, O qubits = %d, width = %d, effective = %d, depth = %d, size = %d"
-            % (
-                counter[subcircuit_idx]["rho"],
-                counter[subcircuit_idx]["O"],
-                counter[subcircuit_idx]["d"],
-                counter[subcircuit_idx]["effective"],
-                counter[subcircuit_idx]["depth"],
-                counter[subcircuit_idx]["size"],
-            )
-        )
-        print(subcircuit)
-    print("Estimated cost = %.3e" % classical_cost, flush=True)
-
-
-def _cuts_parser(
-    cuts: Sequence[tuple[str]], circ: QuantumCircuit
-) -> list[tuple[Qubit, int]]:
-    """
-    Convert cuts to wires.
-
-    Args:
-        cuts: The cuts found by the model (or provided by the user)
-        circ: The quantum circuit the cuts are from
-
-    Returns:
-        The list containing the wires that were cut and the gates
-        that are affected by these cuts
-    """
-    dag = circuit_to_dag(circ)
-    positions: list[tuple[Qubit, int]] = []
-    for position in cuts:
-        if len(position) != 2:
-            raise ValueError(
-                "position variable should be a length 2 sequence: {position}"
-            )
-        source = position[0]
-        dest = position[-1]
-        source_qargs = [
-            (x.split("]")[0] + "]", int(x.split("]")[1])) for x in source.split(" ")
-        ]
-        dest_qargs = [
-            (x.split("]")[0] + "]", int(x.split("]")[1])) for x in dest.split(" ")
-        ]
-        qubit_cut = []
-        for source_qarg in source_qargs:
-            source_qubit, source_multi_Q_gate_idx = source_qarg
-            for dest_qarg in dest_qargs:
-                dest_qubit, dest_multi_Q_gate_idx = dest_qarg
-                if (
-                    source_qubit == dest_qubit
-                    and dest_multi_Q_gate_idx == source_multi_Q_gate_idx + 1
-                ):
-                    qubit_cut.append(source_qubit)
-        # if len(qubit_cut)>1:
-        #     raise Exception('one cut is cutting on multiple qubits')
-        for x in source.split(" "):
-            if x.split("]")[0] + "]" == qubit_cut[0]:
-                source_idx = int(x.split("]")[1])
-        for x in dest.split(" "):
-            if x.split("]")[0] + "]" == qubit_cut[0]:
-                dest_idx = int(x.split("]")[1])
-        multi_Q_gate_idx = max(source_idx, dest_idx)
-
-        wire = None
-        for qubit in circ.qubits:
-            if circ.find_bit(qubit).registers[0][0].name == qubit_cut[0].split("[")[
-                0
-            ] and circ.find_bit(qubit).index == int(
-                qubit_cut[0].split("[")[1].split("]")[0]
-            ):
-                wire = qubit
-        tmp = 0
-        all_Q_gate_idx = None
-        for gate_idx, gate in enumerate(
-            list(dag.nodes_on_wire(wire=wire, only_ops=True))
-        ):
-            if len(gate.qargs) > 1:
-                tmp += 1
-                if tmp == multi_Q_gate_idx:
-                    all_Q_gate_idx = gate_idx
-        if (wire is None) or (all_Q_gate_idx is None):
-            raise ValueError("Something unexpected happened while parsing cuts.")
-        positions.append((wire, all_Q_gate_idx))
-    positions = sorted(positions, reverse=True, key=lambda cut: cut[1])
-    return positions
-
-
-def _subcircuits_parser(
-    subcircuit_gates: list[list[str]], circuit: QuantumCircuit
-) -> tuple[Sequence[QuantumCircuit], dict[Qubit, list[dict[str, int | Qubit]]]]:
-    """
-    Convert the subcircuit gates into quantum circuits and path out the DAGs to enable conversion.
-
-    Args:
-        subcircuit_gates: The gates in the subcircuits
-        circuit: The original circuit
-
-    Returns:
-        A tuple containing he subcircuits and the paths in the quantum circuit DAGs
-    """
-    """
-    Assign the single qubit gates to the closest two-qubit gates
-    """
-
-    def calculate_distance_between_gate(gate_A, gate_B):
-        if len(gate_A.split(" ")) >= len(gate_B.split(" ")):
-            tmp_gate = gate_A
-            gate_A = gate_B
-            gate_B = tmp_gate
-        distance = float("inf")
-        for qarg_A in gate_A.split(" "):
-            qubit_A = qarg_A.split("]")[0] + "]"
-            qgate_A = int(qarg_A.split("]")[-1])
-            for qarg_B in gate_B.split(" "):
-                qubit_B = qarg_B.split("]")[0] + "]"
-                qgate_B = int(qarg_B.split("]")[-1])
-                # print('%s gate %d --> %s gate %d'%(qubit_A,qgate_A,qubit_B,qgate_B))
-                if qubit_A == qubit_B:
-                    distance = min(distance, abs(qgate_B - qgate_A))
-        # print('Distance from %s to %s = %f'%(gate_A,gate_B,distance))
-        return distance
-
-    dag = circuit_to_dag(circuit)
-    qubit_allGate_depths = {x: 0 for x in circuit.qubits}
-    qubit_2qGate_depths = {x: 0 for x in circuit.qubits}
-    gate_depth_encodings = {}
-    # print('Before translation :',subcircuit_gates,flush=True)
-    for op_node in dag.topological_op_nodes():
-        gate_depth_encoding = ""
-        for qarg in op_node.qargs:
-            gate_depth_encoding += "%s[%d]%d " % (
-                circuit.find_bit(qarg).registers[0][0].name,
-                circuit.find_bit(qarg).index,
-                qubit_allGate_depths[qarg],
-            )
-        gate_depth_encoding = gate_depth_encoding[:-1]
-        gate_depth_encodings[op_node] = gate_depth_encoding
-        for qarg in op_node.qargs:
-            qubit_allGate_depths[qarg] += 1
-        if len(op_node.qargs) == 2:
-            MIP_gate_depth_encoding = ""
-            for qarg in op_node.qargs:
-                MIP_gate_depth_encoding += "%s[%d]%d " % (
-                    circuit.find_bit(qarg).registers[0][0].name,
-                    circuit.find_bit(qarg).index,
-                    qubit_2qGate_depths[qarg],
-                )
-                qubit_2qGate_depths[qarg] += 1
-            MIP_gate_depth_encoding = MIP_gate_depth_encoding[:-1]
-            # print('gate_depth_encoding = %s, MIP_gate_depth_encoding = %s'%(gate_depth_encoding,MIP_gate_depth_encoding))
-            for subcircuit_idx in range(len(subcircuit_gates)):
-                for gate_idx in range(len(subcircuit_gates[subcircuit_idx])):
-                    if (
-                        subcircuit_gates[subcircuit_idx][gate_idx]
-                        == MIP_gate_depth_encoding
-                    ):
-                        subcircuit_gates[subcircuit_idx][gate_idx] = gate_depth_encoding
-                        break
-    # print('After translation :',subcircuit_gates,flush=True)
-    subcircuit_op_nodes: dict[int, list[DAGOpNode]] = {
-        x: [] for x in range(len(subcircuit_gates))
-    }
-    subcircuit_sizes = [0 for x in range(len(subcircuit_gates))]
-    complete_path_map: dict[Qubit, list[dict[str, int | Qubit]]] = {}
-    for circuit_qubit in dag.qubits:
-        complete_path_map[circuit_qubit] = []
-        qubit_ops = dag.nodes_on_wire(wire=circuit_qubit, only_ops=True)
-        for qubit_op_idx, qubit_op in enumerate(qubit_ops):
-            gate_depth_encoding = gate_depth_encodings[qubit_op]
-            nearest_subcircuit_idx = -1
-            min_distance = float("inf")
-            for subcircuit_idx in range(len(subcircuit_gates)):
-                distance = float("inf")
-                for gate in subcircuit_gates[subcircuit_idx]:
-                    if len(gate.split(" ")) == 1:
-                        # Do not compare against single qubit gates
-                        continue
-                    else:
-                        distance = min(
-                            distance,
-                            calculate_distance_between_gate(
-                                gate_A=gate_depth_encoding, gate_B=gate
-                            ),
-                        )
-                # print('Distance from %s to subcircuit %d = %f'%(gate_depth_encoding,subcircuit_idx,distance))
-                if distance < min_distance:
-                    min_distance = distance
-                    nearest_subcircuit_idx = subcircuit_idx
-            assert nearest_subcircuit_idx != -1
-            path_element = {
-                "subcircuit_idx": nearest_subcircuit_idx,
-                "subcircuit_qubit": subcircuit_sizes[nearest_subcircuit_idx],
-            }
-            if (
-                len(complete_path_map[circuit_qubit]) == 0
-                or nearest_subcircuit_idx
-                != complete_path_map[circuit_qubit][-1]["subcircuit_idx"]
-            ):
-                # print('{} op #{:d} {:s} encoding = {:s}'.format(circuit_qubit,qubit_op_idx,qubit_op.name,gate_depth_encoding),
-                # 'belongs in subcircuit %d'%nearest_subcircuit_idx)
-                complete_path_map[circuit_qubit].append(path_element)
-                subcircuit_sizes[nearest_subcircuit_idx] += 1
-
-            subcircuit_op_nodes[nearest_subcircuit_idx].append(qubit_op)
-    for circuit_qubit in complete_path_map:
-        # print(circuit_qubit,'-->')
-        for path_element in complete_path_map[circuit_qubit]:
-            path_element_qubit = QuantumRegister(
-                size=subcircuit_sizes[path_element["subcircuit_idx"]], name="q"
-            )[path_element["subcircuit_qubit"]]
-            path_element["subcircuit_qubit"] = path_element_qubit
-            # print(path_element)
-    subcircuits = _generate_subcircuits(
-        subcircuit_op_nodes=subcircuit_op_nodes,
-        complete_path_map=complete_path_map,
-        subcircuit_sizes=subcircuit_sizes,
-        dag=dag,
-    )
-    return subcircuits, complete_path_map
-
-
-def _generate_subcircuits(
-    subcircuit_op_nodes: dict[int, list[DAGOpNode]],
-    complete_path_map: dict[Qubit, list[dict[str, int | Qubit]]],
-    subcircuit_sizes: Sequence[int],
-    dag: DAGCircuit,
-) -> Sequence[QuantumCircuit]:
-    """
-    Generate the subcircuits from given nodes and paths.
-
-    Called in the subcircuit_parser function to convert the found paths and nodes
-    into actual quantum circuit objects.
-
-    Args:
-        subcircuit_op_nodes: The nodes of each of the subcircuits
-        complete_path_map: The complete path through the subcircuits
-        subcircuit_sizes: The number of qubits in each of the subcircuits
-        dag: The dag representation of the input quantum circuit
-
-    Returns:
-        The subcircuits
-    """
-    qubit_pointers = {x: 0 for x in complete_path_map}
-    subcircuits = [QuantumCircuit(x, name="q") for x in subcircuit_sizes]
-    for op_node in dag.topological_op_nodes():
-        subcircuit_idx_list = list(
-            filter(
-                lambda x: op_node in subcircuit_op_nodes[x], subcircuit_op_nodes.keys()
-            )
-        )
-        if len(subcircuit_idx_list) != 1:
-            raise ValueError("A node cannot belong to more than one subcircuit.")
-        subcircuit_idx = subcircuit_idx_list[0]
-        # print('{} belongs in subcircuit {:d}'.format(op_node.qargs,subcircuit_idx))
-        subcircuit_qargs = []
-        for op_node_qarg in op_node.qargs:
-            if (
-                complete_path_map[op_node_qarg][qubit_pointers[op_node_qarg]][
-                    "subcircuit_idx"
-                ]
-                != subcircuit_idx
-            ):
-                qubit_pointers[op_node_qarg] += 1
-            path_element = complete_path_map[op_node_qarg][qubit_pointers[op_node_qarg]]
-            assert path_element["subcircuit_idx"] == subcircuit_idx
-            subcircuit_qargs.append(path_element["subcircuit_qubit"])
-        # print('-->',subcircuit_qargs)
-
-        # mypy doesn't recognize QuantumCircuit as being an Iterable, so we ignore
-        subcircuits[subcircuit_idx].append(  # type: ignore
-            instruction=op_node.op, qargs=subcircuit_qargs, cargs=None
-        )
-    return subcircuits
-
-
-def _get_counter(
-    subcircuits: Sequence[QuantumCircuit],
-    O_rho_pairs: list[tuple[dict[str, int | Qubit], dict[str, int | Qubit]]],
-) -> dict[int, dict[str, int]]:
-    """
-    Create information regarding each of the subcircuit parameters (qubits, width, etc.).
-
-    Args:
-        subcircuits: The list of subcircuits
-        O_rho_pairs: The pairs for each qubit path as generated in the _get_pairs function
-
-    Returns:
-        The resulting dictionary with all parameter information
-    """
-    counter = {}
-    for subcircuit_idx, subcircuit in enumerate(subcircuits):
-        counter[subcircuit_idx] = {
-            "effective": subcircuit.num_qubits,
-            "rho": 0,
-            "O": 0,
-            "d": subcircuit.num_qubits,
-            "depth": subcircuit.depth(),
-            "size": subcircuit.size(),
-        }
-    for pair in O_rho_pairs:
-        if len(pair) != 2:
-            raise ValueError(f"O_rho_pairs must be length 2: {pair}")
-        O_qubit = pair[0]
-        rho_qubit = pair[-1]
-        counter[O_qubit["subcircuit_idx"]]["effective"] -= 1
-        counter[O_qubit["subcircuit_idx"]]["O"] += 1
-        counter[rho_qubit["subcircuit_idx"]]["rho"] += 1
-    return counter
-
-
-def _cost_estimate(counter: dict[int, dict[str, int]]) -> float:
-    """
-    Estimate the cost of processing the subcircuits.
-
-    Args:
-        counter: Dictionary containing information for each of the subcircuits
-
-    Returns:
-        The estimated cost for classical processing
-    """
-    num_cuts = sum([counter[subcircuit_idx]["rho"] for subcircuit_idx in counter])
-    subcircuit_indices = list(counter.keys())
-    num_effective_qubits_list = [
-        counter[subcircuit_idx]["effective"] for subcircuit_idx in subcircuit_indices
-    ]
-    num_effective_qubits, _ = zip(
-        *sorted(zip(num_effective_qubits_list, subcircuit_indices))
-    )
-    classical_cost = 0
-    accumulated_kron_len = 2 ** num_effective_qubits[0]
-    for effective in num_effective_qubits[1:]:
-        accumulated_kron_len *= 2**effective
-        classical_cost += accumulated_kron_len
-    classical_cost *= 4**num_cuts
-    return classical_cost
-
-
-def _get_pairs(
-    complete_path_map: dict[Qubit, list[dict[str, int | Qubit]]]
-) -> list[tuple[dict[str, int | Qubit], dict[str, int | Qubit]]]:
-    """
-    Get all pairs through each path.
-
-    Iterates through the path for each of the qubits and keeps track of the
-    each pair of neigbors.
-
-    Args:
-        complete_path_map: The dictionary containing all path information
-
-    Returns:
-        All pairs for each of the qubit paths
-    """
-    O_rho_pairs = []
-    for input_qubit in complete_path_map:
-        path = complete_path_map[input_qubit]
-        if len(path) > 1:
-            for path_ctr, item in enumerate(path[:-1]):
-                O_qubit_tuple = item
-                rho_qubit_tuple = path[path_ctr + 1]
-                O_rho_pairs.append((O_qubit_tuple, rho_qubit_tuple))
-    return O_rho_pairs
-
-
-def _circuit_stripping(circuit: QuantumCircuit) -> QuantumCircuit:
-    """
-    Remove all single qubit and barrier type gates.
-
-    Args:
-        circuit: The circuit to strip
-
-    Returns:
-        The stripped circuit
-    """
-    # Remove all single qubit gates and barriers in the circuit
-    dag = circuit_to_dag(circuit)
-    stripped_dag = DAGCircuit()
-    [stripped_dag.add_qreg(x) for x in circuit.qregs]
-    for vertex in dag.topological_op_nodes():
-        if len(vertex.qargs) == 2 and vertex.op.name != "barrier":
-            stripped_dag.apply_operation_back(op=vertex.op, qargs=vertex.qargs)
-    return dag_to_circuit(stripped_dag)
-
-
-def _read_circuit(
-    circuit: QuantumCircuit,
-) -> tuple[int, list[tuple[int, int]], dict[str, int], dict[int, str]]:
-    """
-    Read the input circuit to a graph based representation for the MIP model.
-
-    Args:
-        circuit: A stripped circuit to be converted into a DAG like representation
-
-    Returns:
-        A tuple containing the number of vertices, edge list, vertex to vertex id mapping,
-        and vertex id to vertex mapping.
-    """
-    dag = circuit_to_dag(circuit)
-    edges = []
-    node_name_ids = {}
-    id_node_names = {}
-    vertex_ids = {}
-    curr_node_id = 0
-    qubit_gate_counter = {}
-    for qubit in dag.qubits:
-        qubit_gate_counter[qubit] = 0
-    for vertex in dag.topological_op_nodes():
-        if len(vertex.qargs) != 2:
-            raise Exception("vertex does not have 2 qargs!")
-        arg0, arg1 = vertex.qargs
-        vertex_name = "%s[%d]%d %s[%d]%d" % (
-            circuit.find_bit(arg0).registers[0][0].name,
-            circuit.find_bit(arg0).index,
-            qubit_gate_counter[arg0],
-            circuit.find_bit(arg1).registers[0][0].name,
-            circuit.find_bit(arg1).index,
-            qubit_gate_counter[arg1],
-        )
-        qubit_gate_counter[arg0] += 1
-        qubit_gate_counter[arg1] += 1
-        # print(vertex.op.label,vertex_name,curr_node_id)
-        if vertex_name not in node_name_ids and id(vertex) not in vertex_ids:
-            node_name_ids[vertex_name] = curr_node_id
-            id_node_names[curr_node_id] = vertex_name
-            vertex_ids[id(vertex)] = curr_node_id
-            curr_node_id += 1
-
-    for u, v, _ in dag.edges():
-        if isinstance(u, DAGOpNode) and isinstance(v, DAGOpNode):
-            u_id = vertex_ids[id(u)]
-            v_id = vertex_ids[id(v)]
-            edges.append((u_id, v_id))
-
-    n_vertices = dag.size()
-
-    return n_vertices, edges, node_name_ids, id_node_names
diff --git a/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.py b/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.py
deleted file mode 100644
index dcc27a14d..000000000
--- a/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""Contains functions for executing subcircuits."""
-
-from __future__ import annotations
-
-import itertools
-import copy
-from typing import Sequence, Any
-from multiprocessing.pool import ThreadPool
-from warnings import warn
-
-import numpy as np
-
-from qiskit import QuantumCircuit
-from qiskit.utils.deprecation import deprecate_func
-from qiskit.converters import circuit_to_dag, dag_to_circuit
-from qiskit.circuit.library.standard_gates import HGate, SGate, SdgGate, XGate
-from qiskit.primitives import BaseSampler, Sampler as TestSampler
-from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
-from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session, Options
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def run_subcircuit_instances(
-    subcircuits: Sequence[QuantumCircuit],
-    subcircuit_instances: dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-    service: QiskitRuntimeService | None = None,
-    backend_names: Sequence[str] | None = None,
-    options: Sequence[Options] | None = None,
-) -> dict[int, dict[int, np.ndarray]]:
-    """
-    Execute all provided subcircuits.
-
-    Using the backend(s) provided, this executes all the subcircuits to generate the
-    resultant probability vectors.
-    subcircuit_instance_probs[subcircuit_idx][subcircuit_instance_idx] = measured probability
-
-    Args:
-        subcircuits: The list of subcircuits to execute
-        subcircuit_instances: Dictionary containing information about each of the
-            subcircuit instances
-        service: The runtime service
-        backend_names: The backend(s) used to execute the subcircuits
-        options: Options for the runtime execution of subcircuits
-
-    Returns:
-        The probability vectors from each of the subcircuit instances
-    """
-    if backend_names and options:
-        if len(backend_names) != len(options):
-            raise AttributeError(
-                f"The list of backend names is length ({len(backend_names)}), "
-                f"but the list of options is length ({len(options)}). It is ambiguous "
-                "how these options should be applied."
-            )
-    if service:
-        if backend_names:
-            backend_names_repeated: list[str | None] = [
-                backend_names[i % len(backend_names)] for i, _ in enumerate(subcircuits)
-            ]
-            if options is None:
-                options_repeated: list[Options | None] = [None] * len(
-                    backend_names_repeated
-                )
-            else:
-                options_repeated = [
-                    options[i % len(options)] for i, _ in enumerate(subcircuits)
-                ]
-        else:
-            warn(
-                "Please provide a list of `backend_names` alongside the `service`. "
-                "With no backend specified, CutQC defaults to using "
-                "ibmq_qasm_simulator, but cloud simulators are not expected to be "
-                "operational past May 15, 2024. For more details, see: "
-                "https://docs.quantum.ibm.com/announcements/product-updates/2024-03-22-announcement-cloud-simulators-and-lab",
-                stacklevel=2,
-            )
-            backend_names_repeated = ["ibmq_qasm_simulator"] * len(subcircuits)
-            if options:
-                options_repeated = [options[0]] * len(subcircuits)
-            else:
-                options_repeated = [None] * len(subcircuits)
-    else:
-        backend_names_repeated = [None] * len(subcircuits)
-        options_repeated = [None] * len(subcircuits)
-
-    subcircuit_instance_probs: dict[int, dict[int, np.ndarray]] = {}
-    with ThreadPool() as pool:
-        args = [
-            [
-                subcircuit_instances[subcircuit_idx],
-                subcircuit,
-                service,
-                backend_names_repeated[subcircuit_idx],
-                options_repeated[subcircuit_idx],
-            ]
-            for subcircuit_idx, subcircuit in enumerate(subcircuits)
-        ]
-        subcircuit_instance_probs_list = pool.starmap(_run_subcircuit_batch, args)
-
-        for i, partition_batch in enumerate(subcircuit_instance_probs_list):
-            subcircuit_instance_probs[i] = partition_batch
-
-    return subcircuit_instance_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def mutate_measurement_basis(meas: tuple[str, ...]) -> list[tuple[Any, ...]]:
-    """
-    Change of basis for all identity measurements.
-
-    For every identity measurement, it is split into an I and Z measurement.
-    I and Z measurement basis correspond to the same logical circuit.
-
-    Args:
-        meas: The current measurement bases
-
-    Returns:
-        The update measurement bases
-    """
-    if all(x != "I" for x in meas):
-        return [meas]
-    else:
-        mutated_meas = []
-        for x in meas:
-            if x != "I":
-                mutated_meas.append([x])
-            else:
-                mutated_meas.append(["I", "Z"])
-        mutated_meas_out = list(itertools.product(*mutated_meas))
-
-        return mutated_meas_out
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def modify_subcircuit_instance(
-    subcircuit: QuantumCircuit, init: tuple[str, ...], meas: tuple[str, ...]
-) -> QuantumCircuit:
-    """
-    Modify the initialization and measurement bases for a given subcircuit.
-
-    Args:
-        subcircuit: The subcircuit to be modified
-        init: The current initializations
-        meas: The current measement bases
-
-    Returns:
-        The updated circuit, modified so the initialziation
-        and measurement operators are all in the standard computational basis
-
-    Raises:
-        Exeption: One of the inits or meas's are not an acceptable string
-    """
-    subcircuit_dag = circuit_to_dag(subcircuit)
-    subcircuit_instance_dag = copy.deepcopy(subcircuit_dag)
-    for i, x in enumerate(init):
-        q = subcircuit.qubits[i]
-        if x == "zero":
-            continue
-        elif x == "one":
-            subcircuit_instance_dag.apply_operation_front(
-                op=XGate(), qargs=[q], cargs=[]
-            )
-        elif x == "plus":
-            subcircuit_instance_dag.apply_operation_front(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-        elif x == "minus":
-            subcircuit_instance_dag.apply_operation_front(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-            subcircuit_instance_dag.apply_operation_front(
-                op=XGate(), qargs=[q], cargs=[]
-            )
-        elif x == "plusI":
-            subcircuit_instance_dag.apply_operation_front(
-                op=SGate(), qargs=[q], cargs=[]
-            )
-            subcircuit_instance_dag.apply_operation_front(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-        elif x == "minusI":
-            subcircuit_instance_dag.apply_operation_front(
-                op=SGate(), qargs=[q], cargs=[]
-            )
-            subcircuit_instance_dag.apply_operation_front(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-            subcircuit_instance_dag.apply_operation_front(
-                op=XGate(), qargs=[q], cargs=[]
-            )
-        else:
-            raise Exception("Illegal initialization :", x)
-    for i, x in enumerate(meas):
-        q = subcircuit.qubits[i]
-        if x == "I" or x == "comp":
-            continue
-        elif x == "X":
-            subcircuit_instance_dag.apply_operation_back(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-        elif x == "Y":
-            subcircuit_instance_dag.apply_operation_back(
-                op=SdgGate(), qargs=[q], cargs=[]
-            )
-            subcircuit_instance_dag.apply_operation_back(
-                op=HGate(), qargs=[q], cargs=[]
-            )
-        else:
-            raise Exception("Illegal measurement basis:", x)
-    subcircuit_instance_circuit = dag_to_circuit(subcircuit_instance_dag)
-
-    return subcircuit_instance_circuit
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def run_subcircuits_using_sampler(
-    subcircuits: Sequence[QuantumCircuit],
-    sampler: BaseSampler,
-) -> list[np.ndarray]:
-    """
-    Execute the subcircuit(s).
-
-    Args:
-        subcircuit: The subcircuits to be executed
-        sampler: The Sampler to use for executions
-
-    Returns:
-        The probability distributions
-    """
-    for subcircuit in subcircuits:
-        if subcircuit.num_clbits == 0:
-            subcircuit.measure_all()
-
-    quasi_dists = sampler.run(circuits=subcircuits).result().quasi_dists
-
-    all_probabilities_out = []
-    for i, qd in enumerate(quasi_dists):
-        probabilities = qd.nearest_probability_distribution()
-        probabilities_out = np.zeros(2 ** subcircuits[i].num_qubits, dtype=float)
-
-        for state in probabilities:
-            probabilities_out[state] = probabilities[state]
-        all_probabilities_out.append(probabilities_out)
-
-    return all_probabilities_out
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def run_subcircuits(
-    subcircuits: Sequence[QuantumCircuit],
-    service: QiskitRuntimeService | None = None,
-    backend_name: str | None = None,
-    options: Options | None = None,
-) -> list[np.ndarray]:
-    """
-    Execute the subcircuit(s).
-
-    Args:
-        subcircuit: The subcircuits to be executed
-        service: The runtime service
-        backend_name: The backend used to execute the subcircuits
-        options: Options for the runtime execution of subcircuits
-
-    Returns:
-        The probability distributions
-    """
-    if service is not None:
-        session = Session(service=service, backend=backend_name)
-        sampler = Sampler(session=session, options=options)
-        # Transpile circuits
-        backend = service.backend(session.backend())
-        pass_manager = generate_preset_pass_manager(
-            optimization_level=1, backend=backend
-        )
-        subcircuits = pass_manager.run(subcircuits)
-    else:
-        sampler = TestSampler(options=options)
-
-    return run_subcircuits_using_sampler(subcircuits, sampler)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def measure_prob(unmeasured_prob: np.ndarray, meas: tuple[Any, ...]) -> np.ndarray:
-    """
-    Compute the effective probability distribution from the subcircuit distribution.
-
-    Args:
-        unmeasured_prob: The outputs of the subcircuit execution
-        meas: The measurement bases
-
-    Returns:
-        The updated measured probability distribution
-    """
-    if meas.count("comp") == len(meas):
-        return np.array(unmeasured_prob)
-    else:
-        measured_prob = np.zeros(int(2 ** meas.count("comp")))
-        for full_state, p in enumerate(unmeasured_prob):
-            sigma, effective_state = measure_state(full_state=full_state, meas=meas)
-            # TODO: Add states merging here. Change effective_state to merged_bin
-            measured_prob[effective_state] += sigma * p
-
-        return measured_prob
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def measure_state(full_state: int, meas: tuple[Any, ...]) -> tuple[int, int]:
-    """
-    Compute the corresponding effective_state for the given full_state.
-
-    Measured in basis `meas`. Returns sigma (int), effective_state (int) where sigma = +-1
-
-    Args:
-        full_state: The current state (in decimal form)
-        meas: The measurement bases
-
-    Returns:
-        Sigma (defined by the parity of non computational basis 1 measurements) and
-        the effective state (defined by the measurements in the computational basis)
-    """
-    bin_full_state = bin(full_state)[2:].zfill(len(meas))
-    sigma = 1
-    bin_effective_state = ""
-    for meas_bit, meas_basis in zip(bin_full_state, meas[::-1]):
-        if meas_bit == "1" and meas_basis != "I" and meas_basis != "comp":
-            sigma *= -1
-        if meas_basis == "comp":
-            bin_effective_state += meas_bit
-    effective_state = int(bin_effective_state, 2) if bin_effective_state != "" else 0
-
-    return sigma, effective_state
-
-
-def _run_subcircuit_batch(
-    subcircuit_instance: dict[tuple[tuple[str, ...], tuple[Any, ...]], int],
-    subcircuit: QuantumCircuit,
-    service: QiskitRuntimeService | None = None,
-    backend_name: str | None = None,
-    options: Options | None = None,
-):
-    """
-    Execute a circuit using qiskit runtime.
-
-    Args:
-        subcircuit_instances: Dictionary containing information about each of the
-            subcircuit instances
-        subcircuit: The subcircuit to execute
-        service: The runtime service
-        backend_name: The backends used to execute the subcircuit
-        options: Options for the runtime execution of subcircuit
-
-    Returns:
-        The measurement probabilities for the subcircuit batch, as calculated from the
-        runtime execution
-    """
-    subcircuit_instance_probs = {}
-    circuits_to_run = []
-
-    # For each circuit associated with a given subcircuit
-    for init_meas in subcircuit_instance:
-        subcircuit_instance_idx = subcircuit_instance[init_meas]
-
-        # Collect all of the circuits we need to evaluate, ensuring we don't have duplicates
-        if subcircuit_instance_idx not in subcircuit_instance_probs:
-            modified_subcircuit_instance = modify_subcircuit_instance(
-                subcircuit=subcircuit,
-                init=init_meas[0],
-                meas=tuple(init_meas[1]),
-            )
-            circuits_to_run.append(modified_subcircuit_instance)
-            mutated_meas = mutate_measurement_basis(meas=tuple(init_meas[1]))
-            for meas in mutated_meas:
-                mutated_subcircuit_instance_idx = subcircuit_instance[
-                    (init_meas[0], meas)
-                ]
-                # Set a placeholder in the probability dict to prevent duplicate circuits to the Sampler
-                subcircuit_instance_probs[mutated_subcircuit_instance_idx] = np.array(
-                    [0.0]
-                )
-
-    # Run all of our circuits in one batch
-    subcircuit_inst_probs = run_subcircuits(
-        circuits_to_run,
-        service=service,
-        backend_name=backend_name,
-        options=options,
-    )
-
-    # Calculate the measured probabilities
-    unique_subcircuit_check = {}
-    i = 0
-    for init_meas in subcircuit_instance:
-        subcircuit_instance_idx = subcircuit_instance[init_meas]
-        if subcircuit_instance_idx not in unique_subcircuit_check:
-            subcircuit_inst_prob = subcircuit_inst_probs[i]
-            i = i + 1
-            mutated_meas = mutate_measurement_basis(meas=tuple(init_meas[1]))
-            for meas in mutated_meas:
-                measured_prob = measure_prob(
-                    unmeasured_prob=subcircuit_inst_prob, meas=meas
-                )
-                mutated_subcircuit_instance_idx = subcircuit_instance[
-                    (init_meas[0], meas)
-                ]
-                subcircuit_instance_probs[mutated_subcircuit_instance_idx] = (
-                    measured_prob
-                )
-                unique_subcircuit_check[mutated_subcircuit_instance_idx] = True
-
-    return subcircuit_instance_probs
diff --git a/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.py b/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.py
deleted file mode 100644
index 0ff61eb23..000000000
--- a/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.py
+++ /dev/null
@@ -1,522 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""File containing all cutting post processing functionality."""
-
-from __future__ import annotations
-
-import itertools
-import multiprocessing as mp
-from typing import Sequence, Any
-
-import numpy as np
-from qiskit import QuantumCircuit
-from qiskit.utils.deprecation import deprecate_func
-from qiskit.circuit import Qubit
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def get_cut_qubit_pairs(
-    complete_path_map: dict[Qubit, Sequence[dict[str, int | Qubit]]]
-) -> list[tuple[dict[str, Any], dict[str, Any]]]:
-    """
-    Get O-rho cut qubit pairs.
-
-    Iterates through the path for each of the qubits and keeps track of the
-    each pair of neigbors.
-
-    Args:
-        complete_path_map: The dictionary containing all path information
-
-    Returns:
-        All pairs for each of the qubit paths
-    """
-    O_rho_pairs = []
-    for input_qubit in complete_path_map:
-        path = complete_path_map[input_qubit]
-        if len(path) > 1:
-            for path_ctr, item in enumerate(path[:-1]):
-                O_qubit_tuple = item
-                rho_qubit_tuple = path[path_ctr + 1]
-                O_rho_pairs.append((O_qubit_tuple, rho_qubit_tuple))
-
-    return O_rho_pairs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def get_label(label_idx: int, num_cuts: int) -> Sequence[str]:
-    """
-    Get the basis label for each cut point.
-
-    Args:
-        label_idx: The label to be applied for the current basis
-        num_cuts: The number of cuts
-
-    Returns:
-        The list of the labels
-    """
-    assert label_idx < 4**num_cuts
-    basis = ["I", "X", "Y", "Z"]
-    label = []
-    for position in range(num_cuts):
-        digit = label_idx % 4
-        label.append(basis[digit])
-        label_idx = label_idx // 4
-    return label
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def attribute_label(
-    label: Sequence[str],
-    O_rho_pairs: list[tuple[dict[str, Any], dict[str, Any]]],
-    num_subcircuits: int,
-) -> dict[int, dict[str, str]]:
-    """
-    Get the label attributed to each subcircuit.
-
-    subcircuit_label[subcircuit_idx]['init'] = str of init for the cut qubits.
-    subcircuit_label[subcircuit_idx]['meas'] = str of meas for the cut qubits.
-
-    Args:
-        label: The labels of bases for each cut point, as generated by the
-            get_label function
-        O_rho_paris: The pairs of cut qubits, as generated by the get_cut_qubit_pairs
-            function
-        num_subcircuits: The number of subcircuits
-
-    Returns:
-        The dictionary containing the labels applied to each of the subcircuits
-    """
-    subcircuit_label = {
-        subcircuit_idx: {"init": "", "meas": ""}
-        for subcircuit_idx in range(num_subcircuits)
-    }
-    for c, pair in zip(label, O_rho_pairs):
-        if len(pair) != 2:
-            raise ValueError(
-                "O_rho_pairs should only contain sequences of length 2: {pair}"
-            )
-        O_qubit = pair[0]
-        rho_qubit: dict[str, int | Qubit] = pair[-1]
-        subcircuit_label[O_qubit["subcircuit_idx"]]["meas"] += c
-        subcircuit_label[rho_qubit["subcircuit_idx"]]["init"] += c
-    return subcircuit_label
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def fill_label(
-    subcircuit_idx: int,
-    subcircuit: QuantumCircuit,
-    subcircuit_label: dict[str, str],
-    O_rho_pairs: list[tuple[dict[str, Any], dict[str, Any]]],
-) -> tuple[list[str], list[str]]:
-    """
-    Given a subcircuit, its label and O-rho cut qubit pairs fill the full init, meas strings.
-
-    Args:
-        subcircuit_idx: The subcircuit index
-        subcircuit: The subcircuit
-        subcircuit_label: The dictionary containing the applied labels for the bases
-        O_rho_pairs: The list of cut pair qubits
-
-    Returns:
-        A tuple containing the initial input bases and the measurements required
-    """
-    subcircuit_init_label_counter = 0
-    subcircuit_meas_label_counter = 0
-    init = ["zero" for q in range(subcircuit.num_qubits)]
-    meas = ["comp" for q in range(subcircuit.num_qubits)]
-    for pair in O_rho_pairs:
-        if len(pair) != 2:
-            raise ValueError(f"O_rho_pairs should be length 2: {O_rho_pairs}")
-        O_qubit = pair[0]
-        rho_qubit: dict[str, int | Qubit] = pair[-1]
-        if O_qubit["subcircuit_idx"] == subcircuit_idx:
-            qubit_idx = subcircuit.qubits.index(O_qubit["subcircuit_qubit"])
-            meas[qubit_idx] = subcircuit_label["meas"][subcircuit_meas_label_counter]
-            subcircuit_meas_label_counter += 1
-        if rho_qubit["subcircuit_idx"] == subcircuit_idx:
-            qubit_idx = subcircuit.qubits.index(rho_qubit["subcircuit_qubit"])
-            init[qubit_idx] = subcircuit_label["init"][subcircuit_init_label_counter]
-            subcircuit_init_label_counter += 1
-    return init, meas
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def get_init_meas(
-    init_label: Sequence[str], meas_label: Sequence[str]
-) -> list[tuple[tuple[str, ...], tuple[str, ...]]]:
-    """
-    Generate the initial measurements.
-
-    Args:
-        init_label: The list of initial bases
-        meas: The list of measurement bases
-
-    Returns:
-        All subcircuit initial measurements required
-    """
-    init_combinations = []
-    for x in init_label:
-        if x == "zero":
-            init_combinations.append(["zero"])
-        elif x == "I":
-            init_combinations.append(["+zero", "+one"])
-        elif x == "X":
-            init_combinations.append(["2plus", "-zero", "-one"])
-        elif x == "Y":
-            init_combinations.append(["2plusI", "-zero", "-one"])
-        elif x == "Z":
-            init_combinations.append(["+zero", "-one"])
-        else:
-            raise Exception("Illegal initilization symbol :", x)
-    init_combinations = list(itertools.product(*init_combinations))  # type: ignore
-
-    meas_combinations = []
-    for x in meas_label:
-        meas_combinations.append([x])
-    meas_combinations = list(itertools.product(*meas_combinations))  # type: ignore
-
-    subcircuit_init_meas = []
-    for init in init_combinations:
-        for meas in meas_combinations:
-            subcircuit_init_meas.append((tuple(init), tuple(meas)))
-    return subcircuit_init_meas
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def convert_to_physical_init(init: list[str]) -> tuple[int, tuple[str, ...]]:
-    """
-    Convert the initial measurements to the physical representations.
-
-    Args:
-        init: The initial measurements, e.g. ["zero", "2plus"]
-
-    Returns:
-        A tuple containing the coefficient of the physical measurements
-        and the physical measurement bases
-    """
-    coefficient = 1
-    for idx, x in enumerate(init):
-        if x == "zero":
-            continue
-        elif x == "+zero":
-            init[idx] = "zero"
-        elif x == "+one":
-            init[idx] = "one"
-        elif x == "2plus":
-            init[idx] = "plus"
-            coefficient *= 2
-        elif x == "-zero":
-            init[idx] = "zero"
-            coefficient *= -1
-        elif x == "-one":
-            init[idx] = "one"
-            coefficient *= -1
-        elif x == "2plusI":
-            init[idx] = "plusI"
-            coefficient *= 2
-        else:
-            raise Exception("Illegal initilization symbol :", x)
-    return coefficient, tuple(init)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def generate_summation_terms(
-    subcircuits: Sequence[QuantumCircuit],
-    complete_path_map: dict[Qubit, Sequence[dict[str, int | Qubit]]],
-    num_cuts: int,
-) -> tuple[
-    list[dict[int, int]],
-    dict[int, dict[tuple[str, str], tuple[int, Sequence[tuple[int, int]]]]],
-    dict[int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]],
-]:
-    """
-    Generate all summation terms for the final reconstructions.
-
-    Final CutQC reconstruction result = Sum(summation_terms).
-
-    summation_terms (list): [summation_term_0, summation_term_1, ...] --> 4^#cuts elements.
-
-    | summation_term[subcircuit_idx] = subcircuit_entry_idx.
-    | E.g. summation_term = {0:0,1:13,2:7} = Kron(subcircuit_0_entry_0, subcircuit_1_entry_13, subcircuit_2_entry_7).
-
-    | subcircuit_entries[subcircuit_idx][init_label,meas_label] = subcircuit_entry_idx, kronecker_term.
-    | kronecker_term (list): (coefficient, subcircuit_instance_idx).
-    | Add coefficient*subcircuit_instance to subcircuit_entry.
-
-    subcircuit_instances[subcircuit_idx][init,meas] = subcircuit_instance_idx.
-
-    Args:
-        subcircuits: The list of subcircuits
-        complete_path_map: The paths of all the qubits through the circuit DAGs
-        num_cuts: The number of cuts
-
-    Returns:
-        A tuple containing a summation term dict, a subcircuit entry dict,
-        and a subcircuit instances dict
-    """
-    summation_terms = []
-    subcircuit_entries: dict[
-        int, dict[tuple[str, str], tuple[int, Sequence[tuple[int, int]]]]
-    ] = {subcircuit_idx: {} for subcircuit_idx in range(len(subcircuits))}
-    subcircuit_instances: dict[
-        int, dict[tuple[tuple[str, ...], tuple[Any, ...]], int]
-    ] = {subcircuit_idx: {} for subcircuit_idx in range(len(subcircuits))}
-    O_rho_pairs = get_cut_qubit_pairs(complete_path_map=complete_path_map)
-    for summation_term_idx in range(4**num_cuts):
-        label = get_label(label_idx=summation_term_idx, num_cuts=num_cuts)
-        # print('%d/%d summation term:'%(summation_term_idx+1,4**num_cuts),label)
-        summation_term = {}
-        subcircuit_labels = attribute_label(
-            label=label, O_rho_pairs=O_rho_pairs, num_subcircuits=len(subcircuits)
-        )
-        for subcircuit_idx in range(len(subcircuits)):
-            # print('subcircuit %d label :'%subcircuit_idx,subcircuit_labels[subcircuit_idx])
-            subcircuit_entry_key = (
-                subcircuit_labels[subcircuit_idx]["init"],
-                subcircuit_labels[subcircuit_idx]["meas"],
-            )
-            if subcircuit_entry_key in subcircuit_entries[subcircuit_idx]:
-                subcircuit_entry_idx, kronecker_term = subcircuit_entries[
-                    subcircuit_idx
-                ][subcircuit_entry_key]
-                # print('Already in, subcircuit_entry {:d} : {}'.format(subcircuit_entry_idx,kronecker_term))
-            else:
-                subcircuit_full_label = fill_label(
-                    subcircuit_idx=subcircuit_idx,
-                    subcircuit=subcircuits[subcircuit_idx],
-                    subcircuit_label=subcircuit_labels[subcircuit_idx],
-                    O_rho_pairs=O_rho_pairs,
-                )
-                # print('Full label :',subcircuit_full_label)
-                if len(subcircuit_full_label) != 2:
-                    raise ValueError(
-                        f"subcircuit_full_label variable should be a length-2 tuple: {subcircuit_full_label}"
-                    )
-                subcircuit_init_meas = get_init_meas(
-                    init_label=subcircuit_full_label[0],
-                    meas_label=subcircuit_full_label[-1],
-                )
-                kronecker_term = []
-                for init_meas in subcircuit_init_meas:
-                    if len(init_meas) != 2:
-                        raise ValueError(
-                            f"init_meas variable should be a length-2 tuple: {init_meas}"
-                        )
-                    init: tuple[str, ...] = init_meas[0]
-                    meas: tuple[str, ...] = init_meas[-1]
-                    coefficient, init = convert_to_physical_init(
-                        init=list(init_meas[0])
-                    )
-                    if (init, meas) in subcircuit_instances[subcircuit_idx]:
-                        subcircuit_instance_idx = subcircuit_instances[subcircuit_idx][
-                            (init, meas)
-                        ]
-                    else:
-                        subcircuit_instance_idx = len(
-                            subcircuit_instances[subcircuit_idx]
-                        )
-                        subcircuit_instances[subcircuit_idx][
-                            (init, meas)
-                        ] = subcircuit_instance_idx
-                    kronecker_term.append((coefficient, subcircuit_instance_idx))
-                subcircuit_entry_idx = len(subcircuit_entries[subcircuit_idx])
-                subcircuit_entries[subcircuit_idx][subcircuit_entry_key] = (
-                    subcircuit_entry_idx,
-                    kronecker_term,
-                )
-                # print('New subcircuit_entry, {:d} : {}'.format(subcircuit_entry_idx,kronecker_term))
-            summation_term[subcircuit_idx] = subcircuit_entry_idx
-        summation_terms.append(summation_term)
-        # print('summation_term =',summation_term,'\n')
-    return summation_terms, subcircuit_entries, subcircuit_instances
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def naive_compute(
-    subcircuit_order: Sequence[int],
-    summation_terms: Sequence[dict[int, int]],
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]],
-) -> tuple[np.ndarray | None, dict[str, int]]:
-    """
-    Reconstruct the full probability distribution from the subcircuits.
-
-    This function is called within the build function, meant to be used
-    in a multipooling manner.
-
-    Args:
-        subcircuit_order: The order of the subcircuit inputs
-        summation_terms: The summation terms, as generated
-            from generate_summation_terms
-        subcircuit_entry_probs: The input probabilities from each of
-            the subcircuit executions
-
-    Returns:
-        A tuple containing the reconstructed probability distribution
-        and the approximate computational overhead of the function
-    """
-    reconstructed_prob = None
-    overhead = {"additions": 0, "multiplications": 0}
-    for summation_term in summation_terms:
-        summation_term_prob = None
-        for subcircuit_idx in subcircuit_order:
-            subcircuit_entry_idx = summation_term[subcircuit_idx]
-            subcircuit_entry_prob = subcircuit_entry_probs[subcircuit_idx][
-                subcircuit_entry_idx
-            ]
-            if summation_term_prob is None:
-                summation_term_prob = subcircuit_entry_prob
-            else:
-                summation_term_prob = np.kron(
-                    summation_term_prob, subcircuit_entry_prob
-                )
-                overhead["multiplications"] += len(summation_term_prob)
-        if reconstructed_prob is None:
-            reconstructed_prob = summation_term_prob
-        else:
-            reconstructed_prob += summation_term_prob
-            overhead["additions"] += len(reconstructed_prob)
-    return reconstructed_prob, overhead
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def build(
-    summation_terms: Sequence[dict[int, int]],
-    subcircuit_entry_probs: dict[int, dict[int, np.ndarray]],
-    num_cuts: int,
-    num_threads: int,
-) -> tuple[np.ndarray, list[int], dict[str, int]]:
-    """
-    Reconstruct the full probability distribution from the subcircuits.
-
-    Args:
-        summation_terms: The summation terms used to generate the full
-            vector, as generated in generate_summation_terms
-        subcircuit_entry_probs: The probabilities vectors from the
-            subcircuit executions
-        num_cuts: The number of cuts
-        num_threads: The number of threads to use for multithreading
-
-    Returns:
-        A tuple containing the reconstructed probability distribution of the full
-        circuit, the ordering of the distribution, and the computational
-        post-processing overhead
-    """
-    smart_order = sorted(
-        list(subcircuit_entry_probs.keys()),
-        key=lambda subcircuit_idx: len(subcircuit_entry_probs[subcircuit_idx][0]),
-    )
-    args = []
-    for i in range(num_threads * 5):
-        segment_summation_terms = find_process_jobs(
-            jobs=summation_terms, rank=i, num_workers=num_threads * 5
-        )
-        if len(segment_summation_terms) == 0:
-            break
-        arg = (smart_order, segment_summation_terms, subcircuit_entry_probs)
-        args.append(arg)
-    # Why "spawn"?  See https://pythonspeed.com/articles/python-multiprocessing/
-    with mp.get_context("spawn").Pool(num_threads) as pool:
-        results = pool.starmap(naive_compute, args)
-    overhead = {"additions": 0, "multiplications": 0}
-    reconstructed_prob = None
-    for result in results:
-        thread_reconstructed_prob, thread_overhead = result
-        if reconstructed_prob is None:
-            reconstructed_prob = thread_reconstructed_prob
-        else:
-            reconstructed_prob += thread_reconstructed_prob
-        overhead["additions"] += thread_overhead["additions"]
-        overhead["multiplications"] += thread_overhead["multiplications"]
-    reconstructed_prob /= 2**num_cuts
-
-    if reconstructed_prob is None:
-        raise ValueError("Something went wrong during the build.")
-
-    return reconstructed_prob, smart_order, overhead
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def find_process_jobs(
-    jobs: Sequence[Any], rank: int, num_workers: int
-) -> Sequence[Any]:
-    """
-    Split up the total jobs into subjobs to be multithreaded.
-
-    Args:
-        jobs: All summation terms to be used
-        rank: The input thread
-        num_workers: 5 * the number of threads
-
-    Returns:
-        The sublist of jobs to be executed on the given thread
-    """
-    count = int(len(jobs) / num_workers)
-    remainder = len(jobs) % num_workers
-    if rank < remainder:
-        jobs_start = rank * (count + 1)
-        jobs_stop = jobs_start + count + 1
-    else:
-        jobs_start = rank * count + remainder
-        jobs_stop = jobs_start + (count - 1) + 1
-    process_jobs = list(jobs[jobs_start:jobs_stop])
-    return process_jobs
diff --git a/circuit_knitting/cutting/cutqc/wire_cutting_verification.py b/circuit_knitting/cutting/cutqc/wire_cutting_verification.py
deleted file mode 100644
index 31afc8b1b..000000000
--- a/circuit_knitting/cutting/cutqc/wire_cutting_verification.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""File that contains the function to verify the results of the cut circuits."""
-
-from __future__ import annotations
-
-import copy
-import psutil
-from typing import Sequence
-
-import numpy as np
-from qiskit import QuantumCircuit
-from qiskit.circuit import Qubit
-from qiskit.quantum_info import Statevector
-from qiskit_aer import Aer
-from qiskit.utils.deprecation import deprecate_func
-
-from ...utils.conversion import quasi_to_real
-from ...utils.metrics import (
-    chi2_distance,
-    MSE,
-    MAPE,
-    cross_entropy,
-    HOP,
-)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def verify(
-    full_circuit: QuantumCircuit,
-    reconstructed_output: np.ndarray,
-) -> tuple[dict[str, dict[str, float]], Sequence[float]]:
-    """
-    Compare the reconstructed probabilities to the ground truth.
-
-    Executes the original circuit, then measures the distributional differences between this exact
-    result (ground truth) and the reconstructed result from the subcircuits.
-    Provides a variety of metrics to evaluate the differences in the distributions.
-
-    Args:
-        full_circuit: The original quantum circuit that was cut
-        reconstructed_output: The reconstructed probability distribution from the
-            execution of the subcircuits
-
-    Returns:
-        A tuple containing metrics for the ground truth and reconstructed distributions
-    """
-    ground_truth = _evaluate_circuit(circuit=full_circuit)
-    metrics = {}
-    for quasi_conversion_mode in ["nearest", "naive"]:
-        real_probability = quasi_to_real(
-            quasiprobability=reconstructed_output, mode=quasi_conversion_mode
-        )
-
-        chi2 = chi2_distance(target=ground_truth, obs=real_probability)
-        mse = MSE(target=ground_truth, obs=real_probability)
-        mape = MAPE(target=ground_truth, obs=real_probability)
-        ce = cross_entropy(target=ground_truth, obs=real_probability)
-        hop = HOP(target=ground_truth, obs=real_probability)
-        metrics[quasi_conversion_mode] = {
-            "chi2": chi2,
-            "Mean Squared Error": mse,
-            "Mean Absolute Percentage Error": mape,
-            "Cross Entropy": ce,
-            "HOP": hop,
-        }
-    return metrics, ground_truth
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-    additional_msg="Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. ",
-)
-def generate_reconstructed_output(
-    full_circuit: QuantumCircuit,
-    subcircuits: Sequence[QuantumCircuit],
-    unordered: np.ndarray,
-    smart_order: Sequence[int],
-    complete_path_map: dict[Qubit, Sequence[dict[str, int | Qubit]]],
-) -> np.ndarray:
-    """
-    Reorder the probability distribution.
-
-    Args:
-        full_circuit: The original uncut circuit
-        subcircuits: The cut subcircuits
-        unordered: The unordered results of the subcircuits
-        smart_order: The correct ordering of the subcircuits
-        complete_path_map: The path map of the cuts, as defined from the
-            cutting function
-
-    Returns:
-        The reordered and reconstructed probability distribution over the
-        full circuit
-    """
-    subcircuit_out_qubits: dict[int, list[Qubit]] = {
-        subcircuit_idx: [] for subcircuit_idx in smart_order
-    }
-    for input_qubit in complete_path_map:
-        path = complete_path_map[input_qubit]
-        output_qubit = path[-1]
-        subcircuit_out_qubits[output_qubit["subcircuit_idx"]].append(
-            (output_qubit["subcircuit_qubit"], full_circuit.qubits.index(input_qubit))
-        )
-
-    for subcircuit_idx in subcircuit_out_qubits:
-        subcircuit_out_qubits[subcircuit_idx] = sorted(
-            subcircuit_out_qubits[subcircuit_idx],
-            key=lambda x: subcircuits[subcircuit_idx].qubits.index(x[0]),
-            reverse=True,
-        )
-        subcircuit_out_qubits[subcircuit_idx] = [
-            x[1] for x in subcircuit_out_qubits[subcircuit_idx]
-        ]
-
-    unordered_qubit: list[int] = []
-    for subcircuit_idx in smart_order:
-        unordered_qubit += subcircuit_out_qubits[subcircuit_idx]
-
-    reconstructed_output = np.zeros(len(unordered))
-
-    for unordered_state, unordered_p in enumerate(unordered):
-        bin_unordered_state = bin(unordered_state)[2:].zfill(full_circuit.num_qubits)
-        _, ordered_bin_state = zip(
-            *sorted(zip(unordered_qubit, bin_unordered_state), reverse=True)
-        )
-        ordered_bin_state_str = "".join([str(x) for x in ordered_bin_state])
-        ordered_state = int(ordered_bin_state_str, 2)
-        reconstructed_output[ordered_state] = unordered_p
-
-    return np.array(reconstructed_output)
-
-
-def _evaluate_circuit(circuit: QuantumCircuit) -> Sequence[float]:
-    """
-    Compute exact probability vector of given circuit.
-
-    Args:
-        circuit: The circuit to simulate
-
-    Returns:
-        The final probability vector of the circuit
-    """
-    max_memory_mb = psutil.virtual_memory().total >> 20
-    max_memory_mb = int(max_memory_mb / 4 * 3)
-    simulator = Aer.get_backend(
-        "aer_simulator_statevector", max_memory_mb=max_memory_mb
-    )
-    circuit = copy.deepcopy(circuit)
-    circuit.save_state()
-    result = simulator.run(circuit).result()
-    statevector = result.get_statevector(circuit)
-    prob_vector = Statevector(statevector).probabilities()
-
-    return prob_vector
diff --git a/circuit_knitting/utils/__init__.py b/circuit_knitting/utils/__init__.py
index 5c41eab99..ecf71eb3c 100644
--- a/circuit_knitting/utils/__init__.py
+++ b/circuit_knitting/utils/__init__.py
@@ -18,24 +18,12 @@
 
 .. automodule:: circuit_knitting.utils.bitwise
 
-=============================================================
-Conversion (:mod:`circuit_knitting.utils.conversion`)
-=============================================================
-
-.. automodule:: circuit_knitting.utils.conversion
-
 =====================================================================
 Iteration utilities (:mod:`circuit_knitting.utils.iteration`)
 =====================================================================
 
 .. automodule:: circuit_knitting.utils.iteration
 
-=======================================================
-Metrics (:mod:`circuit_knitting.utils.metrics`)
-=======================================================
-
-.. automodule:: circuit_knitting.utils.metrics
-
 ===============================================================================
 Observable grouping (:mod:`circuit_knitting.utils.observable_grouping`)
 ===============================================================================
diff --git a/circuit_knitting/utils/conversion.py b/circuit_knitting/utils/conversion.py
deleted file mode 100644
index 63a88ef6a..000000000
--- a/circuit_knitting/utils/conversion.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""
-Code for converting types of distributions.
-
-.. currentmodule:: circuit_knitting.utils.conversion
-
-.. autosummary::
-   :toctree: ../stubs/
-
-   quasi_to_real
-   nearest_probability_distribution
-   naive_probability_distribution
-   dict_to_array
-"""
-
-import numpy as np
-from qiskit.utils.deprecation import deprecate_func
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def quasi_to_real(quasiprobability, mode):
-    """
-    Convert a quasi probability to a valid probability distribution.
-
-    Args:
-        quasiprobability: The array of quasiprobabilities
-        mode: How to compute the new distribution, either 'nearest' or 'naive'
-
-    Returns:
-        The converted probability distribution
-    """
-    if mode == "nearest":
-        return nearest_probability_distribution(quasiprobability=quasiprobability)
-    elif mode == "naive":
-        return naive_probability_distribution(quasiprobability=quasiprobability)
-    else:
-        raise NotImplementedError("%s conversion is not implemented" % mode)
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def nearest_probability_distribution(quasiprobability):
-    """
-    Convert quasiprobability distribution to the nearest probability distribution.
-
-    Takes a quasiprobability distribution and maps
-    it to the closest probability distribution as defined by
-    the L2-norm.
-
-    Method from Smolin et al., Phys. Rev. Lett. 108, 070502 (2012).
-
-    Args:
-        quasiprobability: The input quasiprobabilities
-
-    Returns:
-        The converted probability distribution
-    """
-    sorted_probs, states = zip(
-        *sorted(zip(quasiprobability, range(len(quasiprobability))))
-    )
-    num_elems = len(sorted_probs)
-    new_probs = np.zeros(num_elems)
-    beta = 0
-    diff = 0
-    for state, prob in zip(states, sorted_probs):
-        temp = prob + beta / num_elems
-        if temp < 0:
-            beta += prob
-            num_elems -= 1
-            diff += prob * prob
-        else:
-            diff += (beta / num_elems) * (beta / num_elems)
-            new_probs[state] = prob + beta / num_elems
-    return new_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def naive_probability_distribution(quasiprobability):
-    """
-    Convert quasiprobability dist to probability dist by zero-ing out negative values.
-
-    Takes a quasiprobability distribution and does the following two steps:
-    1. Update all negative probabilities to 0
-    2. Normalize
-
-    Args:
-        quasiprobability: The input quasiprobabilities
-
-    Returns:
-        The converted probability distribution
-    """
-    new_probs = np.where(quasiprobability < 0, 0, quasiprobability)
-    new_probs /= np.sum(new_probs)
-    return new_probs
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def dict_to_array(distribution_dict, force_prob):
-    """
-    Convert dictionary of shot results to array of distribution.
-
-    Args:
-        distribution_dict: The dictionary containing the shot information
-            from circuit execution
-        force_prob: Whether to force the distribution to be normalized
-
-    Returns:
-        The resulting probability information
-    """
-    state = list(distribution_dict.keys())[0]
-    num_qubits = len(state)
-    num_shots = sum(distribution_dict.values())
-    cnts = np.zeros(2**num_qubits, dtype=float)
-    for state in distribution_dict:
-        cnts[int(state, 2)] = distribution_dict[state]
-    if abs(sum(cnts) - num_shots) > 1:
-        print(
-            "dict_to_array may be wrong, converted counts = {}, input counts = {}".format(
-                sum(cnts), num_shots
-            )
-        )
-    if not force_prob:
-        return cnts
-    else:
-        prob = cnts / num_shots
-        assert abs(sum(prob) - 1) < 1e-10
-        return prob
diff --git a/circuit_knitting/utils/metrics.py b/circuit_knitting/utils/metrics.py
deleted file mode 100644
index 354f56ca5..000000000
--- a/circuit_knitting/utils/metrics.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""
-Functions for comparing array distances.
-
-.. currentmodule:: circuit_knitting.utils.metrics
-
-.. autosummary::
-   :toctree: ../stubs
-
-   chi2_distance
-   MSE
-   MAPE
-   cross_entropy
-   HOP
-"""
-
-import copy
-
-import numpy as np
-
-from qiskit.utils.deprecation import deprecate_func
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def chi2_distance(target, obs):  # noqa: D301
-    r"""
-    Measure the Chi-square distance.
-
-    The Chi-Square distance is a measure of statistically correlation between
-    two feature vectors and is defined as $ \sum_i \frac{(x_i - y_i)^2}{x_i + y_i}$.
-
-    Examples:
-    >>> chi2_distance(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.21645021645021645
-
-    >>> chi2_distance(np.array([0.25, 0.25, 0.25, 0.25]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed distance
-
-    Raises:
-        Exception: The target is not a numpy array or dictionary
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    obs = np.absolute(obs)
-    if isinstance(target, np.ndarray):
-        assert len(target) == len(obs)
-        distance = 0
-        for t, o in zip(target, obs):
-            if abs(t - o) > 1e-10:
-                distance += np.power(t - o, 2) / (t + o)
-    elif isinstance(target, dict):
-        distance = 0
-        for o_idx, o in enumerate(obs):
-            if o_idx in target:
-                t = target[o_idx]
-                if abs(t - o) > 1e-10:
-                    distance += np.power(t - o, 2) / (t + o)
-            else:
-                distance += o
-    else:
-        raise Exception("Illegal target type:", type(target))
-    return distance
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def MSE(target, obs):  # noqa: D301
-    r"""
-    Compute the Mean Squared Error (MSE).
-
-    The MSE is a common metric in fields such as deep learning and is used to
-    measure the squared distance between two vectors via:
-    $\sum_i (x_i - y_i)^2$.
-
-    Example:
-    >>> MSE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.0275
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed MSE
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    if isinstance(target, dict):
-        se = 0
-        for t_idx in target:
-            t = target[t_idx]
-            o = obs[t_idx]
-            se += (t - o) ** 2
-        mse = se / len(obs)
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        target = target.reshape(-1, 1)
-        obs = obs.reshape(-1, 1)
-        squared_diff = (target - obs) ** 2
-        se = np.sum(squared_diff)
-        mse = np.mean(squared_diff)
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        se = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = target[o_idx]
-            se += (t - o) ** 2
-        mse = se / len(obs)
-    else:
-        raise Exception("target type : %s" % type(target))
-    return mse
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def MAPE(target, obs):  # noqa: D301
-    r"""
-    Compute the Mean Absolute Percentage Error (MAPE).
-
-    The MAPE is a scaled metric in the range [0, 100] defining the percentage
-    difference between two vectors via:
-    $ \sum_i \frac{x_i - y_i}{x_i} $.
-
-    Example:
-    >>> MAPE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    91.66666666666659
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed MAPE
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    epsilon = 1e-16
-    if isinstance(target, dict):
-        curr_sum = np.sum(list(target.values()))
-        new_sum = curr_sum + epsilon * len(target)
-        mape = 0
-        for t_idx in target:
-            t = (target[t_idx] + epsilon) / new_sum
-            o = obs[t_idx]
-            mape += abs((t - o) / t)
-        mape /= len(obs)
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        target = target.flatten()
-        target += epsilon
-        target /= np.sum(target)
-        obs = obs.flatten()
-        obs += epsilon
-        obs /= np.sum(obs)
-        mape = np.abs((target - obs) / target)
-        mape = np.mean(mape)
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        curr_sum = np.sum(list(target.values()))
-        new_sum = curr_sum + epsilon * len(target)
-        mape = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = (target[o_idx] + epsilon) / new_sum
-            mape += abs((t - o) / t)
-        mape /= len(obs)
-    else:
-        raise Exception("target type : %s" % type(target))
-    return mape * 100
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def cross_entropy(target, obs):  # noqa: D301
-    r"""
-    Compute the cross entropy between two distributions.
-
-    The cross entropy is a measure of the difference between two probability
-    distributions, defined via:
-    $ -\sum_i x_i \log y_i $.
-
-    Example:
-    >>> cross_entropy(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    1.3862943611198906
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed cross entropy
-
-    Raises:
-        Exception: The target is not a dict
-        Exception: The target and obs are not numpy arrays
-        Exception: The target is not a numpy array and the obs are not a dict
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    if isinstance(target, dict):
-        CE = 0
-        for t_idx in target:
-            t = target[t_idx]
-            o = obs[t_idx]
-            o = o if o > 1e-16 else 1e-16
-            CE += -t * np.log(o)
-        return CE
-    elif isinstance(target, np.ndarray) and isinstance(obs, np.ndarray):
-        obs = np.clip(obs, a_min=1e-16, a_max=None)
-        CE = np.sum(-target * np.log(obs))
-        return CE
-    elif isinstance(target, np.ndarray) and isinstance(obs, dict):
-        CE = 0
-        for o_idx in obs:
-            o = obs[o_idx]
-            t = target[o_idx]
-            o = o if o > 1e-16 else 1e-16
-            CE += -t * np.log(o)
-        return CE
-    else:
-        raise Exception("target type : %s, obs type : %s" % (type(target), type(obs)))
-
-
-@deprecate_func(
-    removal_timeline="no sooner than CKT v0.8.0",
-    since="0.7.0",
-    package_name="circuit-knitting-toolbox",
-)
-def HOP(target, obs):
-    """
-    Compute the Heavy Output Probability (HOP).
-
-    The HOP is an important metric for quantum volume experiments and is defined at the
-    probability that one measures a bitstring above the median target probability.
-
-    Example:
-    >>> HOP(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
-    0.5
-
-    Args:
-        target: The target feature vector
-        obs: The actually observed feature vector
-
-    Returns:
-        The computed HOP
-    """
-    target = copy.deepcopy(target)
-    obs = copy.deepcopy(obs)
-    target_median = np.median(target)
-    hop = 0
-    for t, o in zip(target, obs):
-        if t > target_median:
-            hop += o
-    return hop
diff --git a/compose.yaml b/compose.yaml
index d0df582b3..ada62cb74 100644
--- a/compose.yaml
+++ b/compose.yaml
@@ -7,7 +7,6 @@
 services:
   notebook:
     build: .
-    platform: linux/amd64
     restart: unless-stopped
     # The following line allows Ray to use /dev/shm rather than warn
     # about using /tmp
diff --git a/docs/circuit_cutting/cutqc/README.rst b/docs/circuit_cutting/cutqc/README.rst
deleted file mode 100644
index 902523c4e..000000000
--- a/docs/circuit_cutting/cutqc/README.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-CutQC Tutorials
----------------
-
-- `Tutorial 1 <tutorials/tutorial_1_automatic_cut_finding.ipynb>`__:
-  Perform circuit cutting with automated cut finding, using a
-  mixed-integer programming model.
-- `Tutorial 2 <tutorials/tutorial_2_manual_cutting.ipynb>`__:
-  Perform circuit cutting by manually specifying wire cut locations.
diff --git a/docs/circuit_cutting/cutqc/index.rst b/docs/circuit_cutting/cutqc/index.rst
deleted file mode 100644
index c751c98d2..000000000
--- a/docs/circuit_cutting/cutqc/index.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-#############################################
-CutQC (legacy circuit cutting implementation)
-#############################################
-
-The ``cutqc`` module implements the wire cutting technique and
-automatic cut finding method described in the research paper
-`arXiv:2012.02333 <https://arxiv.org/abs/2012.02333>`_.  Historically,
-this was the original circuit cutting implementation in the Circuit
-Knitting Toolbox.  Going forward, this module is deprecated.  Users
-of the toolbox should use the new circuit cutting interface, instead.
-
-.. _cutqc tutorials:
-
-.. include:: README.rst
-
-.. nbgallery::
-    :glob:
-
-    tutorials/*
-
-
-.. Hiding - Indices and tables
-   :ref:`genindex`
-   :ref:`modindex`
-   :ref:`search`
diff --git a/docs/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb b/docs/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
deleted file mode 100644
index 82672cf65..000000000
--- a/docs/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
+++ /dev/null
@@ -1,597 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c6cd641f",
-   "metadata": {},
-   "source": [
-    "# CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding\n",
-    "\n",
-    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
-    "\n",
-    "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
-    "\n",
-    "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
-    "\n",
-    "This wire cutting technique is comprised of the following basic steps:\n",
-    "\n",
-    "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use an automatic method to find optimal cut(s). See [tutorial 2](tutorial_2_manual_cutting.ipynb) to manually cut a circuit.\n",
-    "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n",
-    "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c17f515",
-   "metadata": {},
-   "source": [
-    "## Create a quantum circuit with Qiskit\n",
-    "\n",
-    "In this case, we'll create a hardware-efficient circuit (`EfficientSU2` from the Qiskit circuit library) with two (linear) entangling layers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eb859bde",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1020.64x491.633 with 1 Axes>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from qiskit.circuit.library import EfficientSU2\n",
-    "\n",
-    "num_qubits = 8\n",
-    "circuit = EfficientSU2(\n",
-    "    num_qubits=num_qubits,\n",
-    "    reps=2,\n",
-    "    entanglement=\"linear\",\n",
-    "    su2_gates=[\"ry\"],\n",
-    ")\n",
-    "\n",
-    "circuit = circuit.decompose()\n",
-    "\n",
-    "params = [(np.pi * i) / 16 for i in range(len(circuit.parameters))]\n",
-    "circuit.assign_parameters(params, inplace=True)\n",
-    "circuit.draw(\"mpl\", fold=-1, scale=0.7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0aa14d2f",
-   "metadata": {},
-   "source": [
-    "## Decompose the circuit with wire cutting\n",
-    "\n",
-    "In this example, we will use an automatic method to find cuts matching our criteria. See [tutorial 2](tutorial_2_manual_cutting.ipynb) for how to manually cut a circuit.\n",
-    "\n",
-    "   * `method='automatic'`: Use a mixed integer programming (MIP) model to find optimal cut(s)\n",
-    "   * `max_subcircuit_width=6`: Only allow subcircuits with 6 qubits or less\n",
-    "   * `max_cuts=2`: Cut the circuit no more than two times\n",
-    "   * `num_subcircuits=[2]`: A list of the number of subcircuits to try, in this case 2 subcircuits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8c11457a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture\n",
-    "\n",
-    "from circuit_knitting.cutting.cutqc import cut_circuit_wires\n",
-    "\n",
-    "cuts = cut_circuit_wires(\n",
-    "    circuit=circuit,\n",
-    "    method=\"automatic\",\n",
-    "    max_subcircuit_width=5,\n",
-    "    max_cuts=2,\n",
-    "    num_subcircuits=[2],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb52c53e",
-   "metadata": {},
-   "source": [
-    "**The results from decompose includes information about the wire cutting process, e.g.,**\n",
-    "\n",
-    "- `subcircuits`: list of `QuantumCircuit` objects for the subcircuits\n",
-    "- `complete_path_map`: a dictionary mapping indices of qubits in original circuit to their indices in the subcircuits. Note that some qubit indices may be mapped to more than one subcircuit.\n",
-    "- `num_cuts`: the number of times the circuit was cut\n",
-    "- `classical_cost`: the final value of the objective function used to find optimal cut(s). The objective function represents the postprocessing cost to reconstruct the original circuit output and is set to be the number of floating-point multiplications involved in the reconstruction. This quantity is also returned in the case of [manual wire cutting](tutorial_2_manual_cutting.ipynb). See [Section 4.1.4 of CutQC](https://doi.org/10.1145/3445814.3446758)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "465733e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['max_subcircuit_width', 'subcircuits', 'complete_path_map', 'num_cuts', 'counter', 'classical_cost'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cuts.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bd643fc",
-   "metadata": {},
-   "source": [
-    "**The two subcircuits produced:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "938f7733",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 523.664x270.9 with 1 Axes>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# visualize the first subcircuit\n",
-    "cuts[\"subcircuits\"][0].draw(\"mpl\", fold=-1, scale=0.6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1c3a5712",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 623.998x270.9 with 1 Axes>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# visualize the second subcircuit\n",
-    "cuts[\"subcircuits\"][1].draw(\"mpl\", fold=-1, scale=0.6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "742ec1e1",
-   "metadata": {},
-   "source": [
-    "## Evaluate the subcircuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "461e57e3",
-   "metadata": {},
-   "source": [
-    "**Set up the Qiskit Runtime Service**\n",
-    "\n",
-    "The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the [Qiskit Runtime documentation](https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/) for more information.\n",
-    "Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the [Qiskit Primitives](https://docs.quantum.ibm.com/api/qiskit/primitives)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "5d1fb2ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_ibm_runtime import QiskitRuntimeService  # noqa: F401\n",
-    "\n",
-    "# Use local versions of the primitives by default.\n",
-    "service = None\n",
-    "\n",
-    "# Uncomment the following line to instead use Qiskit Runtime Service.\n",
-    "# service = QiskitRuntimeService()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cab5dd8",
-   "metadata": {},
-   "source": [
-    "**Configure the Qiskit Runtime Primitive**\n",
-    "\n",
-    "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be [simulator(s) and/or quantum device(s)](https://quantum-computing.ibm.com/services/resources?tab=systems). In this tutorial, two local cores will be used to support each of the parallel backend threads we'll specify below.\n",
-    "\n",
-    "If no service was set up, the `backend_names` argument will be ignored, and Qiskit Primitives will be used with statevector simulator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3cc622d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_ibm_runtime import Options\n",
-    "\n",
-    "# Set the Sampler and runtime options\n",
-    "options = Options(execution={\"shots\": 4000})\n",
-    "\n",
-    "# Use the least busy backend for both threads\n",
-    "backend_names = None\n",
-    "if service is not None:\n",
-    "    backend_names = [service.least_busy().name] * 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e5d9696",
-   "metadata": {},
-   "source": [
-    "**Evaluate the subcircuits on the backend(s)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
-    "\n",
-    "subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n",
-    "\n",
-    "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
-    "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service=service,\n",
-    "#                                                          backend_names=backend_names,\n",
-    "#                                                          options=options,\n",
-    "#                                                         )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66997ed4",
-   "metadata": {},
-   "source": [
-    "**Inspecting the subcircuit results**\n",
-    "\n",
-    "In this case, the original circuit was cut 2 times (we can also get this info from the previous step: `cuts['num_cuts']`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "fa22661e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of subcircuits:  2\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Number of subcircuits: \", len(subcircuit_instance_probabilities))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ee40d04",
-   "metadata": {},
-   "source": [
-    "From these two wire cuts, there are $4^2=16$ variants of the first subcircuit corresponding to the combination of measurement bases: $P_i\\otimes P_j$, for the Paulis $P_i \\in \\{I, X, Y, Z \\}$. And there are $4^2=16$ variants of the second subcircuit corresponding to the combination of initialization states: $|s_i\\rangle\\otimes|s_j\\rangle$, where $|s_i\\rangle \\in \\{ |0\\rangle, |1\\rangle, |+\\rangle |+i\\rangle\\}$. \n",
-    "\n",
-    "\n",
-    "Note that some subcircuit probabilities returned by the evaluate step can be negative (and not sum to unity). This is because the raw probabilities from subcircuits must be modified to account for the measurement bases of ancillary qubits. See Section 3 of [CutQC](https://doi.org/10.1145/3445814.3446758) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7e57f303",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of variants of 1st subcircuit:  16\n",
-      "Number of variants of 2nd subcircuit:  16\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    \"Number of variants of 1st subcircuit: \", len(subcircuit_instance_probabilities[0])\n",
-    ")\n",
-    "print(\n",
-    "    \"Number of variants of 2nd subcircuit: \", len(subcircuit_instance_probabilities[1])\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfdf82a5",
-   "metadata": {},
-   "source": [
-    "The first subcircuit has two ancillary qubits (induced by the two wire cuts) that do not appear in the original circuit. This means that the first subcircuit has $5-2=3$ qubits from the original circuit and a probability distribution of size $2^3=8$. The second subcircuit has 5 qubits, all from the original circuit, and so its probability distribution is size $2^5=32$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3ec4d42c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of 1st subcircuit probability distribution:  8\n",
-      "Size of 2nd subcircuit probability distribution:  32\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    \"Size of 1st subcircuit probability distribution: \",\n",
-    "    len(subcircuit_instance_probabilities[0][0]),\n",
-    ")\n",
-    "print(\n",
-    "    \"Size of 2nd subcircuit probability distribution: \",\n",
-    "    len(subcircuit_instance_probabilities[1][0]),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17e8511c",
-   "metadata": {},
-   "source": [
-    "## Reconstruct the full circuit output\n",
-    "\n",
-    "Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit's full probability distribution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "5aceecc0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture\n",
-    "\n",
-    "from circuit_knitting.cutting.cutqc import (\n",
-    "    reconstruct_full_distribution,\n",
-    ")\n",
-    "\n",
-    "reconstructed_probabilities = reconstruct_full_distribution(\n",
-    "    circuit, subcircuit_instance_probabilities, cuts\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3dbae8e0",
-   "metadata": {},
-   "source": [
-    "**Here are the reconstructed probabilities for the original 8-qubit circuit:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "919958cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Size of reconstructed probability distribution:  256\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    \"Size of reconstructed probability distribution: \", len(reconstructed_probabilities)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f86fe4c",
-   "metadata": {},
-   "source": [
-    "## Verify the results\n",
-    "\n",
-    "If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit's exact probability distribution (ground truth)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from circuit_knitting.cutting.cutqc import verify\n",
-    "\n",
-    "metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03f63d3b",
-   "metadata": {},
-   "source": [
-    "**The verify step includes several metrics**\n",
-    "\n",
-    "For example, the chi square loss is computed. Since we're using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting/utils/metrics.py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "673d3cb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 8.13178352181795e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880309854901524e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.9945381353717198},\n",
-       " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 3.7794080473092745e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880563544629694e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.99453813537172}}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec8c120e",
-   "metadata": {},
-   "source": [
-    "**Visualize both distributions**\n",
-    "\n",
-    "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "c8cc97e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1600x600 with 1 Axes>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from qiskit.visualization import plot_histogram\n",
-    "from qiskit.result import ProbDistribution\n",
-    "\n",
-    "# Create a dict for the reconstructed distribution\n",
-    "reconstructed_distribution = {\n",
-    "    i: prob for i, prob in enumerate(reconstructed_probabilities)\n",
-    "}\n",
-    "\n",
-    "# Represent states as bitstrings (instead of ints)\n",
-    "reconstructed_dict_bitstring = ProbDistribution(\n",
-    "    data=reconstructed_distribution\n",
-    ").binary_probabilities(num_bits=num_qubits)\n",
-    "\n",
-    "\n",
-    "# Create the ground truth distribution dict\n",
-    "exact_distribution = {i: prob for i, prob in enumerate(exact_probabilities)}\n",
-    "\n",
-    "# Represent states as bitstrings (instead of ints)\n",
-    "exact_dict_bitstring = ProbDistribution(data=exact_distribution).binary_probabilities(\n",
-    "    num_bits=num_qubits\n",
-    ")\n",
-    "\n",
-    "# plot a histogram of the distributions\n",
-    "plot_histogram(\n",
-    "    [exact_dict_bitstring, reconstructed_dict_bitstring],\n",
-    "    number_to_keep=8,\n",
-    "    figsize=(16, 6),\n",
-    "    sort=\"asc\",\n",
-    "    legend=[\"Exact\", \"Reconstructed\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efab631d",
-   "metadata": {},
-   "source": [
-    "This code is a Qiskit project.\n",
-    "© Copyright IBM 2022.\n",
-    "\n",
-    "This code is licensed under the Apache License, Version 2.0. You may\n",
-    "obtain a copy of this license in the LICENSE.txt file in the root directory\n",
-    "of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n",
-    "\n",
-    "Any modifications or derivative works of this code must retain this\n",
-    "copyright notice, and modified files need to carry a notice indicating\n",
-    "that they have been altered from the originals."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb b/docs/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
deleted file mode 100644
index 18ba96bcc..000000000
--- a/docs/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
+++ /dev/null
@@ -1,458 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c6cd641f",
-   "metadata": {},
-   "source": [
-    "# CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting\n",
-    "\n",
-    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
-    "\n",
-    "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
-    "\n",
-    "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
-    "\n",
-    "This wire cutting technique is comprised of the following basic steps:\n",
-    "\n",
-    "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use a manual method to specify the cut(s). See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) to automatically cut a circuit.\n",
-    "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n",
-    "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c17f515",
-   "metadata": {},
-   "source": [
-    "## Create a quantum circuit with Qiskit\n",
-    "\n",
-    "In this tutorial, we'll use the example circuit shown in [CutQC](https://dl.acm.org/doi/10.1145/3445814.3446758)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "eb859bde",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 466.456x338.625 with 1 Axes>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from qiskit import QuantumCircuit\n",
-    "\n",
-    "num_qubits = 5\n",
-    "\n",
-    "circuit = QuantumCircuit(num_qubits)\n",
-    "for i in range(num_qubits):\n",
-    "    circuit.h(i)\n",
-    "circuit.cx(0, 1)\n",
-    "for i in range(2, num_qubits):\n",
-    "    circuit.t(i)\n",
-    "circuit.cx(0, 2)\n",
-    "circuit.rx(np.pi / 2, 4)\n",
-    "circuit.rx(np.pi / 2, 0)\n",
-    "circuit.rx(np.pi / 2, 1)\n",
-    "circuit.cx(2, 4)\n",
-    "circuit.t(0)\n",
-    "circuit.t(1)\n",
-    "circuit.cx(2, 3)\n",
-    "circuit.ry(np.pi / 2, 4)\n",
-    "for i in range(num_qubits):\n",
-    "    circuit.h(i)\n",
-    "\n",
-    "circuit.draw(\"mpl\", fold=-1, scale=0.75)"
-   ]
-  },
-  {
-   "attachments": {
-    "how-to-manual-cut.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "0aa14d2f",
-   "metadata": {},
-   "source": [
-    "## Decompose the circuit with wire cutting\n",
-    "\n",
-    "In this example, we will use a manual method to specify the wire cuts. See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) for how to automatically cut a circuit. The figure below shows the steps for producing the `subcircuit_vertices` argument for the `cut_circuit_wires` function.\n",
-    "\n",
-    "   * `method='manual'`: Manually specify the wire cuts\n",
-    "   * `subcircuit_vertices`: A list of lists containing the two-qubit gate indices appearing on either side of the cut(s)\n",
-    "![how-to-manual-cut.png](attachment:how-to-manual-cut.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8c11457a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture\n",
-    "\n",
-    "from circuit_knitting.cutting.cutqc import cut_circuit_wires\n",
-    "\n",
-    "cuts = cut_circuit_wires(\n",
-    "    circuit=circuit, method=\"manual\", subcircuit_vertices=[[0, 1], [2, 3]]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f826b8c",
-   "metadata": {},
-   "source": [
-    "**The two subcircuits produced**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5816c27f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 423.331x170.567 with 1 Axes>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# visualize the first subcircuit\n",
-    "cuts[\"subcircuits\"][0].draw(\"mpl\", fold=-1, scale=0.6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5f605d57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 373.164x170.567 with 1 Axes>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# visualize the second subcircuit\n",
-    "cuts[\"subcircuits\"][1].draw(\"mpl\", fold=-1, scale=0.6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "742ec1e1",
-   "metadata": {},
-   "source": [
-    "## Evaluate the subcircuits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f47fc118",
-   "metadata": {},
-   "source": [
-    "**Set up the Qiskit Runtime Service**\n",
-    "\n",
-    "The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the [Qiskit Runtime documentation](https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/) for more information.\n",
-    "Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the [Qiskit Primitives](https://docs.quantum.ibm.com/api/qiskit/primitives)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7fd84c6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from qiskit_ibm_runtime import (\n",
-    "    QiskitRuntimeService,  # noqa: F401\n",
-    "    Options,\n",
-    ")\n",
-    "\n",
-    "# Use local versions of the primitives by default.\n",
-    "service = None\n",
-    "\n",
-    "# Uncomment the following line to instead use Qiskit Runtime Service.\n",
-    "# service = QiskitRuntimeService()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2f2d1aa",
-   "metadata": {},
-   "source": [
-    "**Configure the Qiskit Runtime Primitive**\n",
-    "\n",
-    "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be [simulator(s) and/or quantum device(s)](https://quantum-computing.ibm.com/services/resources?tab=systems). In this tutorial, two local cores will be used to support each of the parallel backend threads we'll specify below.\n",
-    "\n",
-    "If no service was set up, the `backend_names` argument will be ignored, and Qiskit Primitives will be used with statevector simulator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "aafc01ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the Sampler and runtime options\n",
-    "options = Options(execution={\"shots\": 4000})\n",
-    "\n",
-    "# Use the least busy backend for both threads\n",
-    "backend_names = None\n",
-    "if service is not None:\n",
-    "    backend_names = [service.least_busy().name] * 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1f09e56",
-   "metadata": {},
-   "source": [
-    "**Evaluate the subcircuits on the backend(s)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
-    "\n",
-    "subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n",
-    "\n",
-    "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
-    "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service=service,\n",
-    "#                                                          backend_names=backend_names,\n",
-    "#                                                          options=options,\n",
-    "#                                                         )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5ee37e",
-   "metadata": {},
-   "source": [
-    " **See Tutorial 1 for more info about the subcircuit results.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17e8511c",
-   "metadata": {},
-   "source": [
-    "## Reconstruct the full circuit output\n",
-    "\n",
-    "Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit's full probability distribution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5aceecc0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture\n",
-    "\n",
-    "from circuit_knitting.cutting.cutqc import (\n",
-    "    reconstruct_full_distribution,\n",
-    ")\n",
-    "\n",
-    "reconstructed_probabilities = reconstruct_full_distribution(\n",
-    "    circuit, subcircuit_instance_probabilities, cuts\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40277ef0",
-   "metadata": {},
-   "source": [
-    "## Verify the results\n",
-    "\n",
-    "If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit's exact probability distribution (ground truth)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from circuit_knitting.cutting.cutqc import verify\n",
-    "\n",
-    "metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b220335e",
-   "metadata": {},
-   "source": [
-    "**The verify step includes several metrics**\n",
-    "\n",
-    "For example, the chi square loss is computed. Since we're using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting/utils/metrics.py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8d54b767",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 1.554441697306333e-32,\n",
-       "  'Mean Absolute Percentage Error': 4.748059826691265e-14,\n",
-       "  'Cross Entropy': 2.599681088367844,\n",
-       "  'HOP': 0.9004283905932716},\n",
-       " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 6.5760386594463524e-34,\n",
-       "  'Mean Absolute Percentage Error': 5.260720927719812e-14,\n",
-       "  'Cross Entropy': 2.5996810883678423,\n",
-       "  'HOP': 0.9004283905932736}}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec8c120e",
-   "metadata": {},
-   "source": [
-    "**Visualize both distributions**\n",
-    "\n",
-    "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c8cc97e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x600 with 1 Axes>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from qiskit.visualization import plot_histogram\n",
-    "from qiskit.result import ProbDistribution\n",
-    "\n",
-    "# Create a dict for the reconstructed distribution\n",
-    "reconstructed_distribution = {\n",
-    "    i: prob for i, prob in enumerate(reconstructed_probabilities)\n",
-    "}\n",
-    "\n",
-    "# Represent states as bitstrings (instead of ints)\n",
-    "reconstructed_dict_bitstring = ProbDistribution(\n",
-    "    data=reconstructed_distribution\n",
-    ").binary_probabilities(num_bits=num_qubits)\n",
-    "\n",
-    "\n",
-    "# Create the ground truth distribution dict\n",
-    "exact_distribution = {i: prob for i, prob in enumerate(exact_probabilities)}\n",
-    "\n",
-    "# Represent states as bitstrings (instead of ints)\n",
-    "exact_dict_bitstring = ProbDistribution(data=exact_distribution).binary_probabilities(\n",
-    "    num_bits=num_qubits\n",
-    ")\n",
-    "\n",
-    "# plot a histogram of the distributions\n",
-    "plot_histogram(\n",
-    "    [exact_dict_bitstring, reconstructed_dict_bitstring],\n",
-    "    number_to_keep=8,\n",
-    "    figsize=(16, 6),\n",
-    "    sort=\"asc\",\n",
-    "    legend=[\"Exact\", \"Reconstructed\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d55d9f98",
-   "metadata": {},
-   "source": [
-    "This code is a Qiskit project.\n",
-    "© Copyright IBM 2022.\n",
-    "\n",
-    "This code is licensed under the Apache License, Version 2.0. You may\n",
-    "obtain a copy of this license in the LICENSE.txt file in the root directory\n",
-    "of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n",
-    "\n",
-    "Any modifications or derivative works of this code must retain this\n",
-    "copyright notice, and modified files need to carry a notice indicating\n",
-    "that they have been altered from the originals."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/index.rst b/docs/index.rst
index b47caeb83..246ddc35b 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -24,7 +24,9 @@ If you use the Circuit Knitting Toolbox in your research, please cite it accordi
 .. literalinclude:: ../CITATION.bib
    :language: bibtex
 
-If you are using the entanglement forging tool in CKT version 0.5.0 or earlier, please use `an older version of the citation file <https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/stable/0.5/CITATION.bib>`__ which includes the authors of that tool.
+If you are using the entanglement forging tool in CKT version 0.5 or earlier, please use `an older version of the citation file <https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/stable/0.5/CITATION.bib>`__ which includes the authors of that tool.
+
+If you are using the CutQC tool in CKT version 0.7 or earlier, please use `an older version of the citation file <https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/stable/0.7/CITATION.bib>`__ which includes the authors of that tool.
 
 Developer guide
 ---------------
@@ -49,7 +51,6 @@ Contents
   Cutting Tutorials <circuit_cutting/tutorials/index>
   Cutting Explanatory Material <circuit_cutting/explanation/index>
   Cutting How-To Guides <circuit_cutting/how-tos/index>
-  CutQC (legacy circuit cutting implementation) <circuit_cutting/cutqc/index>
 
 .. toctree::
   :maxdepth: 2
diff --git a/pyproject.toml b/pyproject.toml
index b7aa86a86..fa4cf0687 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,12 +36,6 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-cplex = [
-    # We use the same restrictions in both of the following lines, as there
-    # is no reason for us to install docplex without cplex.
-    "docplex>=2.23.222; python_version < '3.12' and platform_machine != 'arm64'",
-    "cplex>=22.1.0.0; python_version < '3.12' and platform_machine != 'arm64'",
-]
 dev = [
     "circuit-knitting-toolbox[test,nbtest,lint]",
 ]
@@ -83,7 +77,6 @@ docs = [
     "qiskit-sphinx-theme>=1.14.0, <2"
 ]
 notebook-dependencies = [
-    "circuit-knitting-toolbox[cplex]",
     "matplotlib",
     "ipywidgets",
     "pylatexenc",
@@ -127,4 +120,4 @@ enable = [
 
 [tool.pytest.ini_options]
 testpaths = ["./circuit_knitting/", "./test/"]
-addopts = "--doctest-modules -rs --durations=10 --ignore=circuit_knitting/utils/metrics.py --ignore=circuit_knitting/cutting/cutqc"
+addopts = "--doctest-modules -rs --durations=10"
diff --git a/releasenotes/notes/remove-cutqc-fe70369bdbe65668.yaml b/releasenotes/notes/remove-cutqc-fe70369bdbe65668.yaml
new file mode 100644
index 000000000..d164246cb
--- /dev/null
+++ b/releasenotes/notes/remove-cutqc-fe70369bdbe65668.yaml
@@ -0,0 +1,5 @@
+---
+upgrade:
+  - |
+    The ``CutQC`` subpackage has been removed, along with its two associated utility modules, ``circuit_knitting.utils.metrics`` and ``circuit_knitting.utils.conversion``.
+    Users are now encouraged to use the automatic cut-finding and gate/wire cutting from the :mod:`.circuit_knitting.cutting` package.
diff --git a/test/cutting/test_cutqc.py b/test/cutting/test_cutqc.py
deleted file mode 100644
index 96f34a11c..000000000
--- a/test/cutting/test_cutqc.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# This code is a Qiskit project.
-
-# (C) Copyright IBM 2022.
-
-# This code is licensed under the Apache License, Version 2.0. You may
-# obtain a copy of this license in the LICENSE.txt file in the root directory
-# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
-# Any modifications or derivative works of this code must retain this
-# copyright notice, and modified files need to carry a notice indicating
-# that they have been altered from the originals.
-
-"""Tests for circuit_cutting package."""
-
-import unittest
-import pytest
-import importlib.util
-
-import numpy as np
-from qiskit import QuantumCircuit
-
-cplex_available = importlib.util.find_spec("cplex") is not None
-
-
-class TestCircuitCutting(unittest.TestCase):
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-    def setUp(self):
-        qc = QuantumCircuit(5)
-        for i in range(5):
-            qc.h(i)
-        qc.cx(0, 1)
-        for i in range(2, 5):
-            qc.t(i)
-        qc.cx(0, 2)
-        qc.rx(np.pi / 2, 4)
-        qc.rx(np.pi / 2, 0)
-        qc.rx(np.pi / 2, 1)
-        qc.cx(2, 4)
-        qc.t(0)
-        qc.t(1)
-        qc.cx(2, 3)
-        qc.ry(np.pi / 2, 4)
-        for i in range(5):
-            qc.h(i)
-
-        self.circuit = qc
-
-    @unittest.skipIf(not cplex_available, "cplex is not installed")
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-    def test_circuit_cutting_automatic(self):
-        from circuit_knitting.cutting.cutqc import (
-            cut_circuit_wires,
-            evaluate_subcircuits,
-            reconstruct_full_distribution,
-            verify,
-        )
-
-        qc = self.circuit
-        cuts = cut_circuit_wires(
-            circuit=qc,
-            method="automatic",
-            max_subcircuit_width=3,
-            max_subcircuit_cuts=10,
-            max_subcircuit_size=12,
-            max_cuts=10,
-            num_subcircuits=[2],
-        )
-        subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
-        reconstructed_probabilities = reconstruct_full_distribution(
-            qc, subcircuit_instance_probabilities, cuts
-        )
-
-        metrics, _ = verify(qc, reconstructed_probabilities)
-
-        self.assertAlmostEqual(0.0, metrics["nearest"]["Mean Squared Error"])
-
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-    def test_circuit_cutting_manual(self):
-        from circuit_knitting.cutting.cutqc import (
-            cut_circuit_wires,
-            evaluate_subcircuits,
-            reconstruct_full_distribution,
-            verify,
-        )
-
-        qc = self.circuit
-
-        cuts = cut_circuit_wires(
-            circuit=qc, method="manual", subcircuit_vertices=[[0, 1], [2, 3]]
-        )
-        subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
-        reconstructed_probabilities = reconstruct_full_distribution(
-            qc, subcircuit_instance_probabilities, cuts
-        )
-
-        metrics, _ = verify(qc, reconstructed_probabilities)
-
-        self.assertAlmostEqual(0.0, metrics["nearest"]["Mean Squared Error"])
-
-    @unittest.skipIf(not cplex_available, "cplex is not installed")
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-    def test_circuit_cutting_dynamic_definition(self):
-        from circuit_knitting.cutting.cutqc import (
-            cut_circuit_wires,
-            evaluate_subcircuits,
-            create_dd_bin,
-            reconstruct_dd_full_distribution,
-            verify,
-        )
-
-        qc = self.circuit
-
-        cuts = cut_circuit_wires(
-            circuit=qc,
-            method="automatic",
-            max_subcircuit_width=3,
-            max_subcircuit_cuts=10,
-            max_subcircuit_size=12,
-            max_cuts=10,
-            num_subcircuits=[2],
-        )
-        subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
-
-        dd_bins = create_dd_bin(subcircuit_instance_probabilities, cuts, 4, 15)
-
-        dd_prob = reconstruct_dd_full_distribution(self.circuit, cuts, dd_bins)
-        metrics, _ = verify(qc, dd_prob)
-
-        self.assertAlmostEqual(0.0, metrics["nearest"]["Mean Squared Error"])
-
-    @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-    def test_circuit_cutting_dynamic_definition_ghz(self):
-        from circuit_knitting.cutting.cutqc import (
-            cut_circuit_wires,
-            evaluate_subcircuits,
-            create_dd_bin,
-            reconstruct_dd_full_distribution,
-            verify,
-        )
-
-        qc = QuantumCircuit(20, name="ghz")
-        qc.h(0)
-        for i in range(20 - 1):
-            qc.cx(i, i + 1)
-
-        cuts = cut_circuit_wires(
-            circuit=qc,
-            method="manual",
-            subcircuit_vertices=[
-                [0, 1, 2, 3, 4, 5, 6, 7, 8],
-                [9, 10, 11, 12, 13, 14, 15, 16, 17],
-                [18],
-            ],
-        )
-
-        subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
-        dd_bins = create_dd_bin(subcircuit_instance_probabilities, cuts, 10, 5, 4)
-
-        dd_prob = reconstruct_dd_full_distribution(qc, cuts, dd_bins)
-        metrics, _ = verify(qc, dd_prob)
-        self.assertAlmostEqual(0.0, metrics["nearest"]["Mean Squared Error"])
diff --git a/tox.ini b/tox.ini
index cbca18fe4..6d0c4d202 100644
--- a/tox.ini
+++ b/tox.ini
@@ -6,7 +6,6 @@ isolated_build = True
 [testenv]
 extras =
   test
-  cplex
 commands =
   pytest {posargs}
 
@@ -47,12 +46,11 @@ deps =
   coverage>=5.5
 extras =
   test
-  cplex
 commands =
   coverage3 run --source circuit_knitting --parallel-mode -m pytest --run-slow test/ --coverage {posargs}
   coverage3 combine
   coverage3 html
-  coverage3 report --fail-under=100 --show-missing --omit="circuit_knitting/cutting/cutqc/**/*,circuit_knitting/utils/conversion.py,circuit_knitting/utils/metrics.py"
+  coverage3 report --fail-under=100 --show-missing
 
 [testenv:docs]
 extras =