From 5c9738bf12bdd5a9c86dc74c9a0d234cad4d5cd3 Mon Sep 17 00:00:00 2001 From: Kaelyn Ferris <43348706+kaelynj@users.noreply.github.com> Date: Fri, 1 Nov 2024 14:26:06 -0400 Subject: [PATCH] Implement the rest of code review suggestions --- .../qiskit-addons-sqd-get-started.ipynb | 60 ++++++++++++------- docs/guides/qiskit-addons-sqd.mdx | 14 ++++- scripts/nb-tester/requirements.txt | 1 + 3 files changed, 51 insertions(+), 24 deletions(-) diff --git a/docs/guides/qiskit-addons-sqd-get-started.ipynb b/docs/guides/qiskit-addons-sqd-get-started.ipynb index 8c7a2800887..ea4b991b12e 100644 --- a/docs/guides/qiskit-addons-sqd-get-started.ipynb +++ b/docs/guides/qiskit-addons-sqd-get-started.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "b8f25160-5130-4913-9330-648444d5c77f", "metadata": {}, "source": [ "# Getting started with SQD\n", @@ -17,6 +18,7 @@ }, { "cell_type": "markdown", + "id": "db4db859-4190-42a5-b512-1128eeaf82db", "metadata": {}, "source": [ "\n", @@ -1642,11 +1644,12 @@ " -3.317587539899629 16 16 0 0\n", " -77.40622425962903 0 0 0 0\n", " ```\n", - " \n" + " " ] }, { "cell_type": "markdown", + "id": "bf007314-cc6b-4dcf-8678-5fd73df44b34", "metadata": {}, "source": [ "## Prepare molecule information\n", @@ -1657,6 +1660,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "a41617e6-bc0b-48a9-9fe6-de7cfd84f9f6", "metadata": {}, "outputs": [ { @@ -1691,17 +1695,31 @@ }, { "cell_type": "markdown", + "id": "0f6239eb-d7f5-40ff-9e7a-262cf4fbe613", "metadata": {}, "source": [ - "We'll next generate a random set of counts for the configuration recovery loop to post-process. This will simulate the measurement data of a 32-qubit circuit sampled with 10,000 shots. Once the count data has been generated, the `recover_configurations()` method requires that the count data be converted into a matrix of the bitstrings measured at each shot as well as an array of probabilities for each state that was measured." + "We'll next generate a random set of counts for the configuration recovery loop to post-process. This will simulate the measurement data of a 32-qubit circuit sampled with 10,000 shots. Once the count data has been generated, the `recover_configurations()` method requires that the count data be converted into a matrix of the bitstrings measured at each shot as well as an array of probabilities for each state that was measured.\n", + "\n", + "\n", + "The artificial generation of samples is only used as a means to demonstrate the tooling of `qiskit-addon-sqd`. The package is meant to be used as part of a larger workflow wherein an ansatz or other circuit is defined, optimized, and executed using the `Sampler` primitive. The code cell below contains commented out code demonstrating what a typical workflow might look like.\n", + "" ] }, { "cell_type": "code", "execution_count": 2, + "id": "004ea9de-413c-41d5-9208-a6981166f252", "metadata": {}, "outputs": [], "source": [ + "# from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "\n", + "# sampler = Sampler(mode=backend)\n", + "# job = sampler.run([isa_circuit], shots=10_000)\n", + "# primitive_result = job.result()\n", + "# pub_result = primitive_result[0]\n", + "# counts = pub_result.data.meas.get_counts()\n", + "\n", "from qiskit_addon_sqd.counts import generate_counts_uniform\n", "from qiskit_addon_sqd.counts import counts_to_arrays\n", "import numpy as np\n", @@ -1715,6 +1733,7 @@ }, { "cell_type": "markdown", + "id": "c36a8888-42fa-4c60-9290-45d35f585c4e", "metadata": {}, "source": [ "## Run configuration recovery loop\n", @@ -1732,6 +1751,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "d10aeaee-c20f-421c-94d1-a1e829dcaab5", "metadata": {}, "outputs": [], "source": [ @@ -1741,11 +1761,12 @@ "# Eigenstate solver options\n", "NUM_BATCHES = 10\n", "SAMPLES_PER_BATCH = 300\n", - "MAX_DAVIDSON_CYCLES = 200\n" + "MAX_DAVIDSON_CYCLES = 200" ] }, { "cell_type": "markdown", + "id": "d977f0e1-0e21-45d0-8971-92e1773f418b", "metadata": {}, "source": [ "Next, in order to plot the convergence, define arrays to store the approximation of the ground state energy, expectation value of the $\\langle S \\rangle ^2$, and the orbital occupancy of the molecule" @@ -1754,10 +1775,10 @@ { "cell_type": "code", "execution_count": 4, + "id": "a02aaca2-bca2-4531-b49a-3718a37de948", "metadata": {}, "outputs": [], "source": [ - "\n", "# Self-consistent configuration recovery loop\n", "energy_hist = np.zeros((ITERATIONS, NUM_BATCHES)) # energy history\n", "spin_sq_hist = np.zeros((ITERATIONS, NUM_BATCHES)) # spin history\n", @@ -1767,6 +1788,7 @@ }, { "cell_type": "markdown", + "id": "05823f66-cf57-43e0-bd9a-a77c6b6f8786", "metadata": {}, "source": [ "Then we'll run the configuration recovery loop. Each loop consists of three steps:\n", @@ -1779,6 +1801,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "a6d492b5-d86b-43cd-9171-4add0903fe84", "metadata": {}, "outputs": [ { @@ -1803,12 +1826,12 @@ " # On the first iteration, we have no orbital occupancy information from the\n", " # solver, so we just post-select from the full bitstring set based on hamming weight.\n", " if occupancies_bitwise is None:\n", - " bs_mat_tmp = bitstring_matrix_full\n", - " probs_arr_tmp = probs_array_full\n", + " bitstring_matrix_tmp = bitstring_matrix_full\n", + " probs_array_tmp = probs_array_full\n", "\n", " # If we have average orbital occupancy information, we use it to refine the full set of noisy configurations\n", " else:\n", - " bitstring_matrix_tmp, probs_arrary_tmp = recover_configurations(\n", + " bitstring_matrix_tmp, probs_array_tmp = recover_configurations(\n", " bitstring_matrix_full,\n", " probs_array_full,\n", " occupancies_bitwise,\n", @@ -1819,10 +1842,10 @@ "\n", " # Throw out configurations with incorrect particle number in either the spin-up or spin-down systems\n", " batches = postselect_and_subsample(\n", - " bs_mat_tmp,\n", - " probs_arr_tmp,\n", - " hamming_right=num_alpha,\n", - " hamming_left=num_beta,\n", + " bitstring_matrix_tmp,\n", + " probs_array_tmp,\n", + " hamming_right=5,\n", + " hamming_left=5,\n", " samples_per_batch=SAMPLES_PER_BATCH,\n", " num_batches=NUM_BATCHES,\n", " rand_seed=rand_seed,\n", @@ -1863,6 +1886,7 @@ }, { "cell_type": "markdown", + "id": "39b08afb-82f8-4622-b300-4509fd056917", "metadata": {}, "source": [ "### Visualize the results\n", @@ -1873,11 +1897,12 @@ { "cell_type": "code", "execution_count": 6, + "id": "dcd4ed70-89a0-44f4-b4b0-6270bbeb0c13", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1929,19 +1954,12 @@ "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "description": "Get started with the SQD addon and how to post-process results", "kernelspec": { - "display_name": "qiskit_1-0_scratch_space-fyWgEqUn-py3.11", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1955,7 +1973,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3" }, "title": "Getting started with SQD addon" }, diff --git a/docs/guides/qiskit-addons-sqd.mdx b/docs/guides/qiskit-addons-sqd.mdx index db7432d4626..12a2330cd02 100644 --- a/docs/guides/qiskit-addons-sqd.mdx +++ b/docs/guides/qiskit-addons-sqd.mdx @@ -5,7 +5,7 @@ description: Overview of the Sample-based quantum diagonalization (SQD) workflow # Sample-based quantum diagonalization (SQD) -Sample-based quantum diagonalization (SQD) is a technique for finding eigenvalues and eigenvectors of quantum operators, such as the Hamiltonian of a quantum system, using quantum and distributed classical computing together. +Sample-based quantum diagonalization (SQD) is a technique for finding eigenvalues and eigenvectors of quantum operators, such as the Hamiltonian of a quantum system, using quantum and distributed classical computing together. This post-processing technique may be especially useful for users simulating chemical or other quantum systems. Classical computing is used to process samples obtained from a quantum processor, and to project and diagonalize a target Hamiltonian in a subspace spanned by them. This allows SQD to be robust to samples corrupted by quantum noise and manage large Hamiltonians, such as chemical systems with millions of interacting terms, beyond the reach of exact diagonalization methods. @@ -45,7 +45,7 @@ The SQD workflow using self-consistent configuration recovery is explained in de ![SQD diagram depicting configuration recovery, collecting subsamples, and obtaining eigenstates from those subsamples](/images/guides/qiskit-addons/sqd_diagram.png) -Here $\bar{\mathcal{X}}$ is a set of noisy samples containing, in the context of the Hamiltonian being simulated, physical and non-physical configurations (represented as bitstrings) obtained from execution on a QPU. The non-physical configurations are due to noise and can be processed by the `sqd.configuration_recovery.recover_configurations()` method to refine the samples into a new set $\mathcal{X}_R$. +Here $\bar{\mathcal{X}}$ is a set of noisy samples obtained from a QPU which contain, in the context of the Hamiltonian being simulated, physical and non-physical configurations (represented as bitstrings) obtained from execution on a QPU. The non-physical configurations are due to noise and can be processed by the `sqd.configuration_recovery.recover_configurations()` method to refine the samples into a new set $\mathcal{X}_R$. From this set, batches of configurations $\mathcal{S}^{(1)}...\ \mathcal{S}^{(K)}$ are collected and represent samples obtained from different subspaces of the Hamiltonian. These subspace samples are built from a set of basis states, $\mathcal{S}^{(k)}=\{|\mathbf{x}^{(k)}\rangle\}$, and are projections of the system Hamiltonian, $\hat{H}$. This projection is defined as @@ -53,7 +53,15 @@ $$ \hat{H}_{S^{(k)}} = \hat{P}_{\mathcal{S}^{(k)}}\hat{H}\hat{P}_{\mathcal{S}^{( where $\hat{H}_{\mathcal{S}^{(k)}}$ is the Hamiltonian of a given subspace. -The bulk of the SQD workflow lies here where each of these subspace Hamiltonians are diagonalized. The ground states obtained from each of these subspaces, $|\psi^{(k)}\rangle$, and are used to obtain an estimate of a reference vector of occupancies $\mathbf{n}^{(K)}$ averaged over each of the $K$ subspaces and sent back to the configuration recovery step. Then a new set of subspaces are obtained and diagonalized, and this procedure iterates in a self-consistent loop until the ground state energy estimate has converged. +The bulk of the SQD workflow lies here where each of these subspace Hamiltonians are diagonalized. The ground states obtained from each of these subspaces, $|\psi^{(k)}\rangle$, are used to obtain an estimate of a reference vector of occupancies $\mathbf{n}^{(K)}$ averaged over each of the $K$ subspaces and sent back to the configuration recovery step. Then a new set of subspaces are obtained and diagonalized, and this procedure iterates in a self-consistent loop until the ground state energy estimate has converged. + + +## Next steps + + + - Read about basis gates in the [Represent quantum computers](./represent-quantum-computers#basis-gates) topic. + - Apply your knowledge of basis gates to one of these [workflow example tutorials.](https://learning.quantum.ibm.com/catalog/tutorials?category=workflow-example) + ## References diff --git a/scripts/nb-tester/requirements.txt b/scripts/nb-tester/requirements.txt index 7db3c4396ef..bfbc111ef61 100644 --- a/scripts/nb-tester/requirements.txt +++ b/scripts/nb-tester/requirements.txt @@ -6,3 +6,4 @@ qiskit-aer~=0.15.1 qiskit-ibm-runtime~=0.30.0 qiskit-serverless~=0.17.1 qiskit-ibm-catalog~=0.1 +pyscf~=2.7.0