diff --git a/.clang-format b/.clang-format
new file mode 100644
index 0000000..92eae39
--- /dev/null
+++ b/.clang-format
@@ -0,0 +1,22 @@
+---
+Language: Cpp
+BasedOnStyle: Google
+IndentWidth: 4
+ColumnLimit: 120
+AlignAfterOpenBracket: AlwaysBreak
+AllowShortBlocksOnASingleLine: Never
+AllowShortFunctionsOnASingleLine: None
+AllowShortIfStatementsOnASingleLine: Never
+AllowShortLoopsOnASingleLine: false
+AllowShortLambdasOnASingleLine: None
+BinPackArguments: false
+BinPackParameters: false
+
+IncludeCategories:
+ - Regex: '^<.*>$'
+ Priority: 1
+ - Regex: '^"gtest/.*"$'
+ Priority: 2
+ - Regex: '^".*"$'
+ Priority: 3
+...
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..13ce862
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,177 @@
+# Copyright 2021 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: ci
+on:
+ push:
+ branches:
+ - main
+ tags:
+ - v*
+ pull_request:
+ branches:
+ - main
+jobs:
+ build_dist:
+ runs-on: ${{ matrix.os_dist.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os_dist: [
+ {os: ubuntu-latest, dist: cp310-manylinux_x86_64},
+ {os: ubuntu-latest, dist: cp311-manylinux_x86_64},
+ {os: ubuntu-latest, dist: cp312-manylinux_x86_64},
+
+ {os: macos-latest, dist: cp310-macosx_x86_64, macosarch: x86_64},
+ {os: macos-latest, dist: cp311-macosx_x86_64, macosarch: x86_64},
+ {os: macos-latest, dist: cp312-macosx_x86_64, macosarch: x86_64},
+
+ {os: macos-latest, dist: cp310-macosx_arm64, macosarch: arm64},
+ {os: macos-latest, dist: cp311-macosx_arm64, macosarch: arm64},
+ {os: macos-latest, dist: cp312-macosx_arm64, macosarch: arm64},
+
+ # Currently disabled because in CI it gives "DLL load failed" errors I don't understand when testing the wheel.
+ # {os: windows-latest, dist: cp310-win_amd64},
+ # {os: windows-latest, dist: cp311-win_amd64},
+ # {os: windows-latest, dist: cp312-win_amd64},
+ ]
+ env:
+ MACOSX_DEPLOYMENT_TARGET: "10.15"
+ CIBW_BUILD: "${{ matrix.os_dist.dist }}"
+ CIBW_ARCHS_MACOS: "${{ matrix.os_dist.macosarch }}"
+ CIBW_TEST_REQUIRES: pytest stim sinter pygltflib
+ CIBW_TEST_COMMAND: pytest {project}/src
+ steps:
+ - uses: actions/checkout@v3
+ - uses: actions/setup-python@v3
+ - run: python tools/overwrite_dev_versions_with_date.py
+ - run: mkdir -p output/stim
+ - run: mkdir -p output/stimcirq
+ - run: mkdir -p output/sinter
+ - run: python -m pip install pybind11~=2.11.1 cibuildwheel~=2.16.2 setuptools
+ - run: python -m cibuildwheel --print-build-identifiers
+ - run: python -m cibuildwheel --output-dir output/chromobius
+ - run: python setup.py sdist
+ - run: mv dist/* output/chromobius
+ - uses: actions/upload-artifact@v3
+ with:
+ name: dist
+ path: |
+ ./output/chromobius/*
+ check_sdist_installs:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - uses: actions/setup-python@v3
+ - run: python -m pip install pybind11~=2.11.1 cibuildwheel~=2.16.2 setuptools
+ - run: python setup.py sdist
+ - run: pip install dist/*.tar.gz
+ run_main:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v1
+ - run: cmake .
+ - run: make chromobius -j 2
+ - run: out/chromobius --help
+ build_bazel:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v1
+ - uses: bazelbuild/setup-bazelisk@v1
+ - run: bazel build :all
+ - run: bazel test :chromobius_test
+ build_clang:
+ runs-on: ubuntu-20.04
+ steps:
+ - uses: actions/checkout@v1
+ - run: |
+ cd ..
+ git clone https://github.com/google/googletest.git -b release-1.12.1
+ mkdir googletest/build && cd googletest/build
+ cmake .. -DBUILD_GMOCK=OFF
+ make
+ sudo make install
+ - uses: egor-tensin/setup-clang@v1
+ with:
+ version: latest
+ platform: x64
+ - run: cmake . -DCMAKE_C_COMPILER=cc -DCMAKE_CXX_COMPILER=c++
+ - run: cmake --build . --target chromobius chromobius_test chromobius_perf
+ - run: out/chromobius --help
+ perf:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v1
+ - run: cmake .
+ - run: make chromobius_perf -j 2
+ - run: out/chromobius_perf
+ test:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v1
+ - run: |
+ cd ..
+ git clone https://github.com/google/googletest.git -b release-1.12.1
+ mkdir googletest/build && cd googletest/build
+ cmake .. -DBUILD_GMOCK=OFF
+ make
+ sudo make install
+ - run: cmake .
+ - run: make chromobius_test -j 2
+ - run: out/chromobius_test
+ test_o3:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v1
+ - run: |
+ cd ..
+ git clone https://github.com/google/googletest.git -b release-1.12.1
+ mkdir googletest/build && cd googletest/build
+ cmake .. -DBUILD_GMOCK=OFF
+ make
+ sudo make install
+ - run: cmake . -DSIMD_WIDTH=256
+ - run: make chromobius_test_o3 -j 2
+ - run: out/chromobius_test_o3
+ test_generated_docs_are_fresh:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - uses: actions/setup-python@v3
+ - uses: bazelbuild/setup-bazelisk@v1
+ - run: bazel build :chromobius_dev_wheel
+ - run: pip install bazel-bin/chromobius-0.0.dev0-py3-none-any.whl
+ - run: diff <(python tools/gen_chromobius_api_reference.py -dev) doc/chromobius_api_reference.md
+ - run: diff <(python tools/gen_chromobius_stub_file.py -dev) doc/chromobius.pyi
+ - run: python doc/chromobius.pyi
+ test_generated_file_lists_are_fresh:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - run: tools/regen_file_lists.sh /tmp
+ - run: diff /tmp/perf_files file_lists/perf_files
+ - run: diff /tmp/pybind_files file_lists/pybind_files
+ - run: diff /tmp/source_files_no_main file_lists/source_files_no_main
+ - run: diff /tmp/test_files file_lists/test_files
+ test_pybind:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - uses: actions/setup-python@v3
+ - uses: bazelbuild/setup-bazelisk@v1
+ - run: bazel build :chromobius_dev_wheel
+ - run: pip install bazel-bin/chromobius-0.0.dev0-py3-none-any.whl
+ - run: pip install pytest stim sinter pygltflib
+ - run: pytest src
+ - run: tools/doctest_proper.py --module chromobius
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3e4cbbc
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,31 @@
+.idea
+.pytest_cache
+out
+__pycache__
+cmake-build-debug
+.cmake
+CMakeFiles
+CMakeCache.txt
+.ninja_deps
+.ninja_log
+Testing
+_deps
+build.ninja
+cmake_install.cmake
+perf.data
+perf.data.old
+lib
+bazel-bin
+bazel-out
+bazel-testlogs
+bazel-*
+python_build_chromobius
+chromobius.egg-info
+.clwb
+wheelhouse
+build
+package_data
+.eggs
+dist
+MODULE.bazel
+MODULE.bazel.lock
diff --git a/BUILD b/BUILD
new file mode 100644
index 0000000..55db422
--- /dev/null
+++ b/BUILD
@@ -0,0 +1,127 @@
+package(default_visibility = ["//visibility:public"])
+
+load("@rules_python//python:packaging.bzl", "py_wheel")
+
+SOURCE_FILES_NO_MAIN = glob(
+ [
+ "src/**/*.cc",
+ "src/**/*.h",
+ "src/**/*.inl",
+ ],
+ exclude = glob([
+ "src/**/*.test.cc",
+ "src/**/*.test.h",
+ "src/**/*.perf.cc",
+ "src/**/*.perf.h",
+ "src/**/*.pybind.cc",
+ "src/**/*.pybind.h",
+ "src/**/main.cc",
+ ]),
+)
+
+TEST_FILES = glob(
+ [
+ "src/**/*.test.cc",
+ "src/**/*.test.h",
+ ],
+)
+
+PERF_FILES = glob(
+ [
+ "src/**/*.perf.cc",
+ "src/**/*.perf.h",
+ ],
+)
+
+PYBIND_FILES = glob(
+ [
+ "src/**/*.pybind.cc",
+ "src/**/*.pybind.h",
+ ],
+)
+
+cc_binary(
+ name = "chromobius",
+ srcs = SOURCE_FILES_NO_MAIN + glob(["src/**/main.cc"]),
+ copts = [
+ "-std=c++20",
+ "-O3",
+ "-DNDEBUG",
+ ],
+ includes = ["src/"],
+ deps = [
+ "@pymatching//:libpymatching",
+ "@stim//:stim_lib",
+ ],
+)
+
+cc_binary(
+ name = "chromobius_perf",
+ srcs = SOURCE_FILES_NO_MAIN + PERF_FILES,
+ copts = [
+ "-O3",
+ "-std=c++20",
+ "-DNDEBUG",
+ ],
+ data = glob(["test_data/**"]),
+ includes = ["src/"],
+ deps = [
+ "@pymatching//:libpymatching",
+ "@stim//:stim_lib",
+ ],
+)
+
+cc_test(
+ name = "chromobius_test",
+ srcs = SOURCE_FILES_NO_MAIN + TEST_FILES,
+ copts = [
+ "-std=c++20",
+ ],
+ data = glob(["test_data/**"]),
+ includes = ["src/"],
+ deps = [
+ "@gtest",
+ "@gtest//:gtest_main",
+ "@pymatching//:libpymatching",
+ "@stim//:stim_lib",
+ ],
+)
+
+cc_binary(
+ name = "chromobius.so",
+ srcs = SOURCE_FILES_NO_MAIN + PYBIND_FILES,
+ copts = [
+ "-O3",
+ "-std=c++20",
+ "-fvisibility=hidden",
+ "-DNDEBUG",
+ ],
+ includes = ["src/"],
+ linkshared = 1,
+ deps = [
+ "@pybind11",
+ "@pymatching//:libpymatching",
+ "@stim//:stim_lib",
+ ],
+)
+
+genrule(
+ name = "chromobius_wheel_files",
+ srcs = ["doc/chromobius.pyi"],
+ outs = ["chromobius.pyi"],
+ cmd = "cp $(location doc/chromobius.pyi) $@",
+)
+
+py_wheel(
+ name = "chromobius_dev_wheel",
+ distribution = "chromobius",
+ requires = [
+ "numpy",
+ "stim",
+ ],
+ version = "0.0.dev0",
+ deps = [
+ ":chromobius.so",
+ ":chromobius_wheel_files",
+ ],
+)
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 0000000..d31e9a4
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,111 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+cmake_minimum_required(VERSION 3.13)
+project(chromobius)
+include_directories(src)
+set(CMAKE_CXX_STANDARD 20)
+if (NOT DEFINED CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY out)
+endif()
+if (NOT DEFINED CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+ set(CMAKE_LIBRARY_OUTPUT_DIRECTORY out)
+endif()
+if (NOT DEFINED CMAKE_ARCHIVE_OUTPUT_DIRECTORY)
+ set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY out)
+endif()
+if (NOT DEFINED CMAKE_OSX_DEPLOYMENT_TARGET)
+ set(CMAKE_OSX_DEPLOYMENT_TARGET 10.15)
+endif()
+
+# Make changes to file_lists trigger a reconfigure.
+set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS file_lists/source_files_no_main)
+set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS file_lists/test_files)
+set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS file_lists/perf_files)
+set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS file_lists/pybind_files)
+file(STRINGS file_lists/source_files_no_main SOURCE_FILES_NO_MAIN)
+file(STRINGS file_lists/test_files TEST_FILES)
+file(STRINGS file_lists/perf_files PERF_FILES)
+file(STRINGS file_lists/pybind_files PYBIND_FILES)
+
+set(SIMD_WIDTH 128)
+include(FetchContent)
+FetchContent_Declare(stim
+ GIT_REPOSITORY https://github.com/quantumlib/stim.git
+ GIT_TAG 3e38d12d0a0fb3022646b694137b733a4700d300)
+FetchContent_GetProperties(stim)
+if(NOT stim_POPULATED)
+ FetchContent_Populate(stim)
+ add_subdirectory(${stim_SOURCE_DIR})
+endif()
+
+FetchContent_Declare(pymatching
+ GIT_REPOSITORY https://github.com/oscarhiggott/pymatching.git
+ GIT_TAG 40dcf8c01273ff7e23a7105d5cdc410ada067001)
+FetchContent_GetProperties(pymatching)
+if(NOT pymatching_POPULATED)
+ FetchContent_Populate(pymatching)
+ add_subdirectory(${pymatching_SOURCE_DIR})
+endif()
+
+add_executable(chromobius src/main.cc ${SOURCE_FILES_NO_MAIN} ${PYMATCHING_SOURCE_FILES_NO_MAIN})
+target_compile_options(chromobius PRIVATE -O3 -Wall -Wpedantic)
+target_link_options(chromobius PRIVATE -O3)
+target_link_libraries(chromobius libstim libpymatching)
+install(TARGETS chromobius RUNTIME DESTINATION bin)
+
+add_library(libchromobius ${SOURCE_FILES_NO_MAIN})
+set_target_properties(libchromobius PROPERTIES PREFIX "")
+target_include_directories(libchromobius PUBLIC src)
+target_link_libraries(libchromobius PRIVATE libstim libpymatching)
+if(NOT(MSVC))
+ target_compile_options(libchromobius PRIVATE -O3 -Wall -Wpedantic -fPIC -fno-strict-aliasing)
+ target_link_options(libchromobius PRIVATE -O3)
+else()
+ target_compile_options(libchromobius PRIVATE)
+endif()
+install(TARGETS libchromobius LIBRARY DESTINATION)
+install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/src/" DESTINATION "include" FILES_MATCHING PATTERN "*.h" PATTERN "*.inl")
+
+add_executable(chromobius_perf ${SOURCE_FILES_NO_MAIN} ${PYMATCHING_SOURCE_FILES_NO_MAIN} ${PERF_FILES})
+target_compile_options(chromobius_perf PRIVATE -Wall -Wpedantic -O3 -g -fno-omit-frame-pointer -DNDEBUG)
+target_link_options(chromobius_perf PRIVATE -pthread)
+target_link_libraries(chromobius_perf PRIVATE libstim libpymatching)
+
+find_package(GTest QUIET)
+if(${GTest_FOUND})
+ add_executable(chromobius_test ${SOURCE_FILES_NO_MAIN} ${PYMATCHING_SOURCE_FILES_NO_MAIN} ${TEST_FILES})
+ target_link_libraries(chromobius_test GTest::gtest GTest::gtest_main libstim libpymatching)
+ target_compile_options(chromobius_test PRIVATE -Wall -Wpedantic -g -fno-omit-frame-pointer -fno-strict-aliasing -fsanitize=undefined -fsanitize=address)
+ target_link_options(chromobius_test PRIVATE -g -fno-omit-frame-pointer -fsanitize=undefined -fsanitize=address)
+
+ add_executable(chromobius_test_o3 ${SOURCE_FILES_NO_MAIN} ${PYMATCHING_SOURCE_FILES_NO_MAIN} ${TEST_FILES})
+ target_link_libraries(chromobius_test_o3 GTest::gtest GTest::gtest_main libstim libpymatching)
+ target_compile_options(chromobius_test_o3 PRIVATE -O3 -Wall -Wpedantic -fno-strict-aliasing)
+ target_link_options(chromobius_test_o3 PRIVATE)
+else()
+ message("WARNING: Skipped chromobius_test target. `GTest` not found. To fix, follow Standalone CMake Project install instructions at https://github.com/google/googletest/blob/master/googletest/README.md")
+endif()
+
+find_package(Python COMPONENTS Interpreter Development)
+find_package(pybind11 CONFIG)
+if ((${pybind11_FOUND} AND ${Python_FOUND}) OR "$ENV{CMAKE_FORCE_PYBIND_CHROMOBIUS}")
+ pybind11_add_module(chromobius_pybind ${PYBIND_FILES} ${SOURCE_FILES_NO_MAIN})
+ set_target_properties(chromobius_pybind PROPERTIES OUTPUT_NAME chromobius)
+ target_compile_options(chromobius_pybind PRIVATE -O3 -DNDEBUG)
+ target_link_libraries(chromobius_pybind PRIVATE libstim libpymatching)
+ target_link_options(chromobius_pybind PRIVATE -O3)
+else()
+ message("WARNING: Skipped chromobius_pybind target. `pybind11` not found. To fix, install pybind11. On debian based distributions, the package name is `pybind11-dev`")
+endif()
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..bc23aae
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,33 @@
+# How to contribute
+
+We'd love to accept your patches and contributions to this project.
+
+## Before you begin
+
+### Sign our Contributor License Agreement
+
+Contributions to this project must be accompanied by a
+[Contributor License Agreement](https://cla.developers.google.com/about) (CLA).
+You (or your employer) retain the copyright to your contribution; this simply
+gives us permission to use and redistribute your contributions as part of the
+project.
+
+If you or your current employer have already signed the Google CLA (even if it
+was for a different project), you probably don't need to do it again.
+
+Visit to see your current agreements or to
+sign a new one.
+
+### Review our community guidelines
+
+This project follows
+[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
+
+## Contribution process
+
+### Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,202 @@
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
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+ Contribution(s) alone or by combination of their Contribution(s)
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+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
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+ Licensed under the Apache License, Version 2.0 (the "License");
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+ Unless required by applicable law or agreed to in writing, software
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..12379f6
--- /dev/null
+++ b/README.md
@@ -0,0 +1,104 @@
+# Chromobius: color code decoder
+
+Chromobius is an implementation of a ["mobius decoder"](https://arxiv.org/abs/2108.11395), which approximates the color code decoding problem as a minimum weight matching problem.
+Chromobius uses [PyMatching](https://github.com/oscarhiggott/PyMatching/) to solve the minimum weight matching problem.
+
+See ((((the paper "New circuits and an open source decoder for the color code")))) for more details on how Chromobius works.
+
+## How to use Chromobius
+
+See the [**getting started notebook**](doc/getting_started.ipynb).
+
+Also see the [python api reference](doc/chromobius_api_reference.md).
+
+Programmers who want to edit and build Chromobius can check the [developer documentation](doc/developers.md).
+
+## Example Snippets
+
+### Decoding a shot with Chromobius
+
+From python:
+
+```python
+import stim
+import chromobius
+import numpy as np
+
+def count_mistakes(circuit: stim.Circuit, shots: int) -> int:
+ # Sample the circuit.
+ dets, actual_obs_flips = circuit.compile_detector_sampler().sample(
+ shots=shots,
+ separate_observables=True,
+ bit_packed=True,
+ )
+
+ # Decode with Chromobius.
+ decoder = chromobius.compile_decoder_for_dem(circuit.detector_error_model())
+ predicted_obs_flips = decoder.predict_obs_flips_from_dets_bit_packed(dets)
+
+ # Count mistakes.
+ return np.count_nonzero(np.any(predicted_obs_flips != actual_obs_flips, axis=1))
+```
+
+From the command line:
+
+```bash
+# Sample shots of detectors and observable flips.
+stim detect \
+ --shots 100000 \
+ --in "example_circuit.stim" \
+ --out "dets.b8" \
+ --out_format "b8" \
+ --obs_out "obs_actual.txt" \
+ --obs_out_format "01"
+
+# Extract a detector error model used to configure Chromobius.
+stim analyze_errors \
+ --in "example_circuit.stim" \
+ --fold_loops \
+ --out "dem.dem"
+
+# Decode the shots.
+chromobius predict \
+ --dem "dem.dem" \
+ --in "dets.b8" \
+ --in_format "b8" \
+ --out "obs_predicted.txt" \
+ --out_format "01"
+
+# Count the number of shots with a prediction mistake.
+paste obs_actual.txt obs_predicted.txt \
+ | grep -Pv "^([01]*)\\s*\\1$" \
+ | wc -l
+```
+
+From python using sinter:
+
+```python
+import sinter
+import chromobius
+import os
+
+tasks: list[sinter.Task] = ...
+stats: list[sinter.TaskStats] = sinter.collect(
+ decoders=["chromobius"],
+ custom_decoders=chromobius.sinter_decoders(),
+ tasks=tasks,
+ num_workers=os.cpu_count(),
+ max_shots=100_000,
+ max_errors=100,
+)
+```
+
+From the command line using sinter:
+
+```bash
+sinter collect \
+ --circuits "example_circuit.stim" \
+ --decoders chromobius \
+ --custom_decoders_module_function "chromobius:sinter_decoders" \
+ --max_shots 100_000 \
+ --max_errors 100
+ --processes auto \
+ --save_resume_filepath "stats.csv" \
+```
diff --git a/WORKSPACE b/WORKSPACE
new file mode 100644
index 0000000..29e3ebb
--- /dev/null
+++ b/WORKSPACE
@@ -0,0 +1,50 @@
+load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
+load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
+
+git_repository(
+ name = "stim",
+ commit = "3e38d12d0a0fb3022646b694137b733a4700d300",
+ remote = "https://github.com/quantumlib/stim.git",
+)
+
+git_repository(
+ name = "pymatching",
+ commit = "40dcf8c01273ff7e23a7105d5cdc410ada067001",
+ remote = "https://github.com/oscarhiggott/pymatching.git",
+)
+
+http_archive(
+ name = "gtest",
+ sha256 = "353571c2440176ded91c2de6d6cd88ddd41401d14692ec1f99e35d013feda55a",
+ strip_prefix = "googletest-release-1.11.0",
+ urls = ["https://github.com/google/googletest/archive/refs/tags/release-1.11.0.zip"],
+)
+
+http_archive(
+ name = "pybind11",
+ build_file = "@pybind11_bazel//:pybind11.BUILD",
+ sha256 = "832e2f309c57da9c1e6d4542dedd34b24e4192ecb4d62f6f4866a737454c9970",
+ strip_prefix = "pybind11-2.10.4",
+ urls = ["https://github.com/pybind/pybind11/archive/v2.10.4.tar.gz"],
+)
+
+http_archive(
+ name = "pybind11_bazel",
+ sha256 = "e8355ee56c2ff772334b4bfa22be17c709e5573f6d1d561c7176312156c27bd4",
+ strip_prefix = "pybind11_bazel-2.11.1",
+ urls = ["https://github.com/pybind/pybind11_bazel/releases/download/v2.11.1/pybind11_bazel-2.11.1.tar.gz"],
+)
+
+http_archive(
+ name = "rules_python",
+ sha256 = "0a8003b044294d7840ac7d9d73eef05d6ceb682d7516781a4ec62eeb34702578",
+ strip_prefix = "rules_python-0.24.0",
+ url = "https://github.com/bazelbuild/rules_python/releases/download/0.24.0/rules_python-0.24.0.tar.gz",
+)
+
+load("@rules_python//python:repositories.bzl", "py_repositories")
+load("@pybind11_bazel//:python_configure.bzl", "python_configure")
+
+py_repositories()
+
+python_configure(name = "local_config_python")
diff --git a/assets/ablated_stabilizers.png b/assets/ablated_stabilizers.png
new file mode 100644
index 0000000..6481247
Binary files /dev/null and b/assets/ablated_stabilizers.png differ
diff --git a/assets/cmp_transit_from_prev.png b/assets/cmp_transit_from_prev.png
new file mode 100644
index 0000000..93dd357
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diff --git a/assets/color2surface.png b/assets/color2surface.png
new file mode 100644
index 0000000..6f9dd1a
Binary files /dev/null and b/assets/color2surface.png differ
diff --git a/assets/color_code_layouts.png b/assets/color_code_layouts.png
new file mode 100644
index 0000000..3c0a69f
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diff --git a/assets/color_code_layouts.svg b/assets/color_code_layouts.svg
new file mode 100644
index 0000000..c77cff4
--- /dev/null
+++ b/assets/color_code_layouts.svg
@@ -0,0 +1,15641 @@
+
+
+
+
diff --git a/assets/example_boundary_match.png b/assets/example_boundary_match.png
new file mode 100644
index 0000000..2f35e45
Binary files /dev/null and b/assets/example_boundary_match.png differ
diff --git a/assets/generated/cmp_transit.png b/assets/generated/cmp_transit.png
new file mode 100644
index 0000000..4c3a13b
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diff --git a/assets/generated/compare_X.png b/assets/generated/compare_X.png
new file mode 100644
index 0000000..5a18f7b
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diff --git a/assets/generated/compare_XZ.png b/assets/generated/compare_XZ.png
new file mode 100644
index 0000000..f256f2c
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diff --git a/assets/generated/compare_Z.png b/assets/generated/compare_Z.png
new file mode 100644
index 0000000..15c2678
Binary files /dev/null and b/assets/generated/compare_Z.png differ
diff --git a/assets/generated/midout_error_X.png b/assets/generated/midout_error_X.png
new file mode 100644
index 0000000..0860f25
Binary files /dev/null and b/assets/generated/midout_error_X.png differ
diff --git a/assets/generated/midout_error_XZ.png b/assets/generated/midout_error_XZ.png
new file mode 100644
index 0000000..040c189
Binary files /dev/null and b/assets/generated/midout_error_XZ.png differ
diff --git a/assets/generated/midout_error_Z.png b/assets/generated/midout_error_Z.png
new file mode 100644
index 0000000..da312df
Binary files /dev/null and b/assets/generated/midout_error_Z.png differ
diff --git a/assets/generated/midout_footprint_X.png b/assets/generated/midout_footprint_X.png
new file mode 100644
index 0000000..f29f1a4
Binary files /dev/null and b/assets/generated/midout_footprint_X.png differ
diff --git a/assets/generated/midout_footprint_XZ.png b/assets/generated/midout_footprint_XZ.png
new file mode 100644
index 0000000..f8528e1
Binary files /dev/null and b/assets/generated/midout_footprint_XZ.png differ
diff --git a/assets/generated/midout_footprint_Z.png b/assets/generated/midout_footprint_Z.png
new file mode 100644
index 0000000..db1a449
Binary files /dev/null and b/assets/generated/midout_footprint_Z.png differ
diff --git a/assets/generated/phenom.png b/assets/generated/phenom.png
new file mode 100644
index 0000000..97d429d
Binary files /dev/null and b/assets/generated/phenom.png differ
diff --git a/assets/generated/prev_work_comparison_0.0001.png b/assets/generated/prev_work_comparison_0.0001.png
new file mode 100644
index 0000000..6117e8b
Binary files /dev/null and b/assets/generated/prev_work_comparison_0.0001.png differ
diff --git a/assets/generated/prev_work_comparison_0.0002.png b/assets/generated/prev_work_comparison_0.0002.png
new file mode 100644
index 0000000..cfd9ac0
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diff --git a/assets/generated/prev_work_comparison_0.0003.png b/assets/generated/prev_work_comparison_0.0003.png
new file mode 100644
index 0000000..f868819
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diff --git a/assets/generated/prev_work_comparison_0.0005.png b/assets/generated/prev_work_comparison_0.0005.png
new file mode 100644
index 0000000..1438fa1
Binary files /dev/null and b/assets/generated/prev_work_comparison_0.0005.png differ
diff --git a/assets/generated/prev_work_comparison_0.001.png b/assets/generated/prev_work_comparison_0.001.png
new file mode 100644
index 0000000..f1ef0a3
Binary files /dev/null and b/assets/generated/prev_work_comparison_0.001.png differ
diff --git a/assets/generated/prev_work_comparison_0.002.png b/assets/generated/prev_work_comparison_0.002.png
new file mode 100644
index 0000000..196ceca
Binary files /dev/null and b/assets/generated/prev_work_comparison_0.002.png differ
diff --git a/assets/generated/stats-xz-combo.csv b/assets/generated/stats-xz-combo.csv
new file mode 100644
index 0000000..bf50ada
--- /dev/null
+++ b/assets/generated/stats-xz-combo.csv
@@ -0,0 +1,896 @@
+ shots, errors, discards, seconds,decoder,strong_id,json_metadata,custom_counts
+ 8053, 1006, 0, 0.131,chromobius,dc8a8b0b38a83885b96b6529a9f5f5dea87ed11e5e5890cb772ab86084758d92,"{""c"":""midout_color_code_488_X"",""d"":3,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":9,""r"":12}",
+ 5802, 1019, 0, 0.068,chromobius,06ac46fd8ef9b844361b0a124023d7120d0c67ca82275995ddbf94ca8e468c8d,"{""c"":""midout_color_code_488_Z"",""d"":3,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":9,""r"":12}",
+ 5802, 1617, 0, 0.199,chromobius,06ac46fd8ef9b844361b0a124023d7120d0c67ca82275995ddbf94ca8e468c8d*dc8a8b0b38a83885b96b6529a9f5f5dea87ed11e5e5890cb772ab86084758d92,"{""c"":""midout_color_code_488_XZ"",""d"":3,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":9,""r"":12}",
+ 6936, 1017, 0, 0.734,chromobius,d857db1f0debac8705a37faf9ca51802434e633b05931b885d7bb4b5d6806348,"{""c"":""midout_color_code_488_X"",""d"":5,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":21,""r"":20}",
+ 10813, 1064, 0, 0.392,chromobius,26658b46f4d6a5781df30e418ab964678b33635e4e6cdde7d7c631edd3028139,"{""c"":""midout_color_code_488_Z"",""d"":5,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":21,""r"":20}",
+ 6936, 1599, 0, 1.13,chromobius,d857db1f0debac8705a37faf9ca51802434e633b05931b885d7bb4b5d6806348*26658b46f4d6a5781df30e418ab964678b33635e4e6cdde7d7c631edd3028139,"{""c"":""midout_color_code_488_XZ"",""d"":5,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":21,""r"":20}",
+ 14735, 1113, 0, 1.77,chromobius,731c367d069afbafeff01aa21ddd44bdb90bd0df09f60b50c04ff778da7e5356,"{""c"":""midout_color_code_488_X"",""d"":7,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":37,""r"":28}",
+ 13426, 1032, 0, 1.44,chromobius,bba7a1eaa8e80184a2256ab3034a6f35c82f2beb9105984dcd00186efa04cd6b,"{""c"":""midout_color_code_488_Z"",""d"":7,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":37,""r"":28}",
+ 13426, 1968, 0, 3.21,chromobius,bba7a1eaa8e80184a2256ab3034a6f35c82f2beb9105984dcd00186efa04cd6b*731c367d069afbafeff01aa21ddd44bdb90bd0df09f60b50c04ff778da7e5356,"{""c"":""midout_color_code_488_XZ"",""d"":7,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":37,""r"":28}",
+ 8875, 1000, 0, 3.02,chromobius,870453b363ca6ebda36742c661a40d79fb8ba27a61940ed187b4da9d394a7e9d,"{""c"":""midout_color_code_488_X"",""d"":9,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":57,""r"":36}",
+ 9200, 1002, 0, 3.35,chromobius,5dce626311a7429b8d8e5909ff8d311f581e4ad1e06a9b12f0c6dd947d8e0b8b,"{""c"":""midout_color_code_488_Z"",""d"":9,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":57,""r"":36}",
+ 8875, 1858, 0, 6.37,chromobius,870453b363ca6ebda36742c661a40d79fb8ba27a61940ed187b4da9d394a7e9d*5dce626311a7429b8d8e5909ff8d311f581e4ad1e06a9b12f0c6dd947d8e0b8b,"{""c"":""midout_color_code_488_XZ"",""d"":9,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":57,""r"":36}",
+ 33386, 1038, 0, 15.9,chromobius,4edb9408d768deb425edec64ccd5d27be493eafa42a4ab0bc391af1a22e8eccd,"{""c"":""midout_color_code_488_X"",""d"":11,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":81,""r"":44}",
+ 33346, 1029, 0, 15.3,chromobius,48b3baae33e8d622401e5ab598e3cd2f3a77abad37a118134173092fa4403f1d,"{""c"":""midout_color_code_488_Z"",""d"":11,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":81,""r"":44}",
+ 33346, 2034, 0, 31.2,chromobius,48b3baae33e8d622401e5ab598e3cd2f3a77abad37a118134173092fa4403f1d*4edb9408d768deb425edec64ccd5d27be493eafa42a4ab0bc391af1a22e8eccd,"{""c"":""midout_color_code_488_XZ"",""d"":11,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":81,""r"":44}",
+ 78076, 1079, 0, 55.2,chromobius,f234ea89f3e23b569626df94bb720fcb0264dcfb302846b03db2b92292f84eab,"{""c"":""midout_color_code_488_X"",""d"":13,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":109,""r"":52}",
+ 77400, 1049, 0, 46.0,chromobius,9e014120f7100adf533620defd113a27f0792f943333f61dee82f59ce0aad258,"{""c"":""midout_color_code_488_Z"",""d"":13,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":109,""r"":52}",
+ 77400, 2104, 0, 101.2,chromobius,9e014120f7100adf533620defd113a27f0792f943333f61dee82f59ce0aad258*f234ea89f3e23b569626df94bb720fcb0264dcfb302846b03db2b92292f84eab,"{""c"":""midout_color_code_488_XZ"",""d"":13,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":109,""r"":52}",
+ 105451, 1046, 0, 110.3,chromobius,3ec32b1797d0b0c61572ccf7e2257d8c9f31d1510c00c523b48f6c5fe8bba6f9,"{""c"":""midout_color_code_488_X"",""d"":15,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":141,""r"":60}",
+ 108873, 1042, 0, 120.5,chromobius,74f930788405cc1b93b97a80305b89e60db81ef0a05a0921d7d689be5b625a72,"{""c"":""midout_color_code_488_Z"",""d"":15,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":141,""r"":60}",
+ 105451, 2045, 0, 230.8,chromobius,3ec32b1797d0b0c61572ccf7e2257d8c9f31d1510c00c523b48f6c5fe8bba6f9*74f930788405cc1b93b97a80305b89e60db81ef0a05a0921d7d689be5b625a72,"{""c"":""midout_color_code_488_XZ"",""d"":15,""gates"":""all"",""noise"":""uniform"",""p"":0.001,""q"":141,""r"":60}",
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diff --git a/assets/generated/superdense_error_XZ.png b/assets/generated/superdense_error_XZ.png
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diff --git a/assets/generated/superdense_error_Z.png b/assets/generated/superdense_error_Z.png
new file mode 100644
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diff --git a/assets/generated/superdense_footprint_X.png b/assets/generated/superdense_footprint_X.png
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diff --git a/assets/generated/superdense_footprint_XZ.png b/assets/generated/superdense_footprint_XZ.png
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diff --git a/assets/generated/superdense_footprint_Z.png b/assets/generated/superdense_footprint_Z.png
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diff --git a/assets/generated/timing_d.png b/assets/generated/timing_d.png
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diff --git a/assets/generated/timing_d_full.png b/assets/generated/timing_d_full.png
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diff --git a/assets/generated/timing_r.png b/assets/generated/timing_r.png
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diff --git a/assets/generated/timing_r_full.png b/assets/generated/timing_r_full.png
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diff --git a/assets/generated/timing_rq.png b/assets/generated/timing_rq.png
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diff --git a/assets/generated/timing_rq_full.png b/assets/generated/timing_rq_full.png
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diff --git a/assets/generated/toric.png b/assets/generated/toric.png
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diff --git a/assets/midout-detslice-cycle.png b/assets/midout-detslice-cycle.png
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diff --git a/assets/midout-detslice-cycle.svg b/assets/midout-detslice-cycle.svg
new file mode 100644
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--- /dev/null
+++ b/assets/midout-detslice-cycle.svg
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new file mode 100644
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--- /dev/null
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new file mode 100644
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diff --git a/assets/perf.png b/assets/perf.png
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diff --git a/assets/pyramid_code.png b/assets/pyramid_code.png
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diff --git a/assets/pyramid_code.svg b/assets/pyramid_code.svg
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--- /dev/null
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diff --git a/assets/stats.csv b/assets/stats.csv
new file mode 100644
index 0000000..be99d6a
--- /dev/null
+++ b/assets/stats.csv
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+ shots, errors, discards, seconds,decoder,strong_id,json_metadata,custom_counts
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+ 541921, 0, 0, 1.00,chromobius,5eb68c16485d6f8b15c5c4a2d58ed611918a600547c075f57a41074cf39f3010,"{""c"":""transit_color_code"",""d"":13,""noise"":""uniform"",""p"":0.005,""q"":128,""r"":1}","{""detection_events"":1215352}"
+ 393952, 0, 0, 1.00,chromobius,0c37128a20dffc05159428863d30a72df07fd9d4564a22a7951bd9c2decea286,"{""c"":""transit_color_code"",""d"":13,""noise"":""uniform"",""p"":0.007,""q"":128,""r"":1}","{""detection_events"":1226694}"
+ 272025, 0, 0, 1.00,chromobius,27ee77ec1ac977718a169d50cce58297a6ceb34dd2fff45ce6e96ce116b4cba6,"{""c"":""transit_color_code"",""d"":13,""noise"":""uniform"",""p"":0.01,""q"":128,""r"":1}","{""detection_events"":1203376}"
diff --git a/doc/chromobius.pyi b/doc/chromobius.pyi
new file mode 100644
index 0000000..869d50d
--- /dev/null
+++ b/doc/chromobius.pyi
@@ -0,0 +1,226 @@
+"""Chromobius (Development Version): an implementation of the mobius color code decoder."""
+# (This a stubs file describing the classes and methods in stim.)
+from __future__ import annotations
+from typing import overload, TYPE_CHECKING, Any, Iterable
+if TYPE_CHECKING:
+ import io
+ import pathlib
+ import numpy as np
+ import stim
+ import sinter
+ import chromobius
+class CompiledDecoder:
+ """A chromobius decoder ready to predict observable flips from detection events.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.CompiledDecoder.from_dem(dem)
+ """
+ @staticmethod
+ def from_dem(
+ dem: stim.DetectorErrorModel,
+ ) -> chromobius.CompiledDecoder:
+ """Compiles a decoder for a stim detector error model.
+
+ Args:
+ dem: A stim detector error model. The detector error model must satisfy:
+ 1. Basis+Color annotations. Every detector that appears in an error
+ must specify coordinate data including a fourth coordinate. The 4th
+ coordinate indicates the basis and color of the detector with the
+ convention:
+ 0 = Red X
+ 1 = Green X
+ 2 = Blue X
+ 3 = Red Z
+ 4 = Green Z
+ 5 = Blue Z
+ 2. Rainbow triplets. Bulk errors with three symptoms in one basis should
+ have one symptom of each color. Errors with three symptoms that
+ repeat a color will cause an exception unless they can be decomposed
+ into other basic errors.
+ 3. Movable excitations. It needs to be possible to combine bulk errors
+ to form simpler errors with one or two symptoms that can be used to
+ move or destroy excitations. If bulk errors don't have this
+ property, decoding will fail when attempting to lift a solution
+ from the matcher requires dragging an excitation along a path but
+ there's no way to move the excitation along that path.
+ 4. Matchable-avoids-color. In parts of the dem that correspond to a
+ matchable code, at least one of the colors must be avoided.
+ Otherwise the matcher may be given a problem that can be solved
+ locally, but when lifting it needs to be solved non-locally.
+
+ Returns:
+ A decoder object that can be used to predict observable flips from
+ detection event samples.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.CompiledDecoder.from_dem(dem)
+ """
+ @staticmethod
+ def predict_obs_flips_from_dets_bit_packed(
+ dets: np.ndarray,
+ ) -> np.ndarray:
+ """Predicts observable flips from detection events.
+
+ Args:
+ dets: A bit packed numpy array of detection event data. The array can either
+ be 1-dimensional (a single shot to decode) or 2-dimensional (multiple
+ shots to decode, with the first axis being the shot axis and the second
+ axis being the detection event byte axis).
+
+ The array's dtype must be np.uint8. If you have an array of dtype
+ np.bool_, you have data that's not bit packed. You can pack it by
+ using `np.packbits(array, bitorder='little')`. But ideally you
+ should attempt to never have unpacked data in the first place,
+ since it's 8x larger which can be a large performance loss. For
+ example, stim's sampler methods all have a `bit_packed=True` argument
+ that cause them to return bit packed data.
+
+ Returns:
+ A bit packed numpy array of observable flip data. The array will have
+ the same number of dimensions as the dets argument.
+
+ If dets is a 1D array, then the result has:
+ shape = (math.ceil(num_obs / 8),)
+ dtype = np.uint8
+ If dets is a 2D array, then the result has:
+ shape = (dets.shape[0], math.ceil(num_obs / 8),)
+ dtype = np.uint8
+
+ To determine if the observable with index k was flipped in shot s, compute:
+ `bool((result[s, k // 8] >> (k % 8)) & 1)`
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+ >>> import numpy as np
+
+ >>> repetition_color_code = stim.Circuit('''
+ ... # Apply noise.
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... # Measure three-body stabilizers to catch errors.
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5 Z4*Z5*Z6 Z5*Z6*Z7
+ ...
+ ... # Annotate detectors, with a coloring in the 4th coordinate.
+ ... DETECTOR(0, 0, 0, 2) rec[-6]
+ ... DETECTOR(1, 0, 0, 0) rec[-5]
+ ... DETECTOR(2, 0, 0, 1) rec[-4]
+ ... DETECTOR(3, 0, 0, 2) rec[-3]
+ ... DETECTOR(4, 0, 0, 0) rec[-2]
+ ... DETECTOR(5, 0, 0, 1) rec[-1]
+ ...
+ ... # Check on the message.
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''')
+
+ >>> # Sample the circuit.
+ >>> shots = 4096
+ >>> sampler = repetition_color_code.compile_detector_sampler()
+ >>> dets, actual_obs_flips = sampler.sample(
+ ... shots=shots,
+ ... separate_observables=True,
+ ... bit_packed=True,
+ ... )
+
+ >>> # Decode with Chromobius.
+ >>> dem = repetition_color_code.detector_error_model()
+ >>> decoder = chromobius.compile_decoder_for_dem(dem)
+ >>> predicted_flips = decoder.predict_obs_flips_from_dets_bit_packed(dets)
+
+ >>> # Count mistakes.
+ >>> differences = np.any(predicted_flips != actual_obs_flips, axis=1)
+ >>> mistakes = np.count_nonzero(differences)
+ >>> assert mistakes < shots / 5
+ """
+def compile_decoder_for_dem(
+ dem: stim.DetectorErrorModel,
+) -> chromobius.CompiledDecoder:
+ """Compiles a decoder for a stim detector error model.
+
+ Args:
+ dem: A stim detector error model. The detector error model must satisfy:
+ 1. Basis+Color annotations. Every detector that appears in an error
+ must specify coordinate data including a fourth coordinate. The 4th
+ coordinate indicates the basis and color of the detector with the
+ convention:
+ 0 = Red X
+ 1 = Green X
+ 2 = Blue X
+ 3 = Red Z
+ 4 = Green Z
+ 5 = Blue Z
+ 2. Rainbow triplets. Bulk errors with three symptoms in one basis should
+ have one symptom of each color. Errors with three symptoms that
+ repeat a color will cause an exception unless they can be decomposed
+ into other basic errors.
+ 3. Movable excitations. It needs to be possible to combine bulk errors
+ to form simpler errors with one or two symptoms that can be used to
+ move or destroy excitations. If bulk errors don't have this
+ property, decoding will fail when attempting to lift a solution
+ from the matcher requires dragging an excitation along a path but
+ there's no way to move the excitation along that path.
+ 4. Matchable-avoids-color. In parts of the dem that correspond to a
+ matchable code, at least one of the colors must be avoided.
+ Otherwise the matcher may be given a problem that can be solved
+ locally, but when lifting it needs to be solved non-locally.
+
+ Returns:
+ A decoder object that can be used to predict observable flips from
+ detection event samples.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.compile_decoder_for_dem(dem)
+ """
+def sinter_decoders() -> dict[str, sinter.Decoder]:
+ """A dictionary describing chromobius to sinter.
+
+ Giving the result of this function to the `custom_decoders` argument of
+ `sinter.collect` will tell sinter about the decoder 'chromobius'. On the
+ command line, the equivalent argument is
+ `--custom_decoders 'chromobius:sinter_decoders'`.
+
+ Returns:
+ The dict `{'chromobius': }`.
+ """
diff --git a/doc/chromobius_api_reference.md b/doc/chromobius_api_reference.md
new file mode 100644
index 0000000..968137e
--- /dev/null
+++ b/doc/chromobius_api_reference.md
@@ -0,0 +1,266 @@
+# Chromobius (Development Version) API Reference
+
+## Index
+- ``
+ - [`chromobius.compile_decoder_for_dem`](#chromobius.compile_decoder_for_dem)
+ - [`chromobius.sinter_decoders`](#chromobius.sinter_decoders)
+- [`chromobius.CompiledDecoder`](#chromobius.CompiledDecoder)
+ - [`chromobius.CompiledDecoder.from_dem`](#chromobius.CompiledDecoder.from_dem)
+ - [`chromobius.CompiledDecoder.predict_obs_flips_from_dets_bit_packed`](#chromobius.CompiledDecoder.predict_obs_flips_from_dets_bit_packed)
+```python
+# Types used by the method definitions.
+from typing import overload, TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
+import io
+import pathlib
+import numpy as np
+```
+
+
+```python
+# chromobius.compile_decoder_for_dem
+
+# (at top-level in the chromobius module)
+def compile_decoder_for_dem(
+ dem: stim.DetectorErrorModel,
+) -> chromobius.CompiledDecoder:
+ """Compiles a decoder for a stim detector error model.
+
+ Args:
+ dem: A stim detector error model. The detector error model must satisfy:
+ 1. Basis+Color annotations. Every detector that appears in an error
+ must specify coordinate data including a fourth coordinate. The 4th
+ coordinate indicates the basis and color of the detector with the
+ convention:
+ 0 = Red X
+ 1 = Green X
+ 2 = Blue X
+ 3 = Red Z
+ 4 = Green Z
+ 5 = Blue Z
+ 2. Rainbow triplets. Bulk errors with three symptoms in one basis should
+ have one symptom of each color. Errors with three symptoms that
+ repeat a color will cause an exception unless they can be decomposed
+ into other basic errors.
+ 3. Movable excitations. It needs to be possible to combine bulk errors
+ to form simpler errors with one or two symptoms that can be used to
+ move or destroy excitations. If bulk errors don't have this
+ property, decoding will fail when attempting to lift a solution
+ from the matcher requires dragging an excitation along a path but
+ there's no way to move the excitation along that path.
+ 4. Matchable-avoids-color. In parts of the dem that correspond to a
+ matchable code, at least one of the colors must be avoided.
+ Otherwise the matcher may be given a problem that can be solved
+ locally, but when lifting it needs to be solved non-locally.
+
+ Returns:
+ A decoder object that can be used to predict observable flips from
+ detection event samples.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.compile_decoder_for_dem(dem)
+ """
+```
+
+
+```python
+# chromobius.sinter_decoders
+
+# (at top-level in the chromobius module)
+def sinter_decoders() -> dict[str, sinter.Decoder]:
+ """A dictionary describing chromobius to sinter.
+
+ Giving the result of this function to the `custom_decoders` argument of
+ `sinter.collect` will tell sinter about the decoder 'chromobius'. On the
+ command line, the equivalent argument is
+ `--custom_decoders 'chromobius:sinter_decoders'`.
+
+ Returns:
+ The dict `{'chromobius': }`.
+ """
+```
+
+
+```python
+# chromobius.CompiledDecoder
+
+# (at top-level in the chromobius module)
+class CompiledDecoder:
+ """A chromobius decoder ready to predict observable flips from detection events.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.CompiledDecoder.from_dem(dem)
+ """
+```
+
+
+```python
+# chromobius.CompiledDecoder.from_dem
+
+# (in class chromobius.CompiledDecoder)
+@staticmethod
+def from_dem(
+ dem: stim.DetectorErrorModel,
+) -> chromobius.CompiledDecoder:
+ """Compiles a decoder for a stim detector error model.
+
+ Args:
+ dem: A stim detector error model. The detector error model must satisfy:
+ 1. Basis+Color annotations. Every detector that appears in an error
+ must specify coordinate data including a fourth coordinate. The 4th
+ coordinate indicates the basis and color of the detector with the
+ convention:
+ 0 = Red X
+ 1 = Green X
+ 2 = Blue X
+ 3 = Red Z
+ 4 = Green Z
+ 5 = Blue Z
+ 2. Rainbow triplets. Bulk errors with three symptoms in one basis should
+ have one symptom of each color. Errors with three symptoms that
+ repeat a color will cause an exception unless they can be decomposed
+ into other basic errors.
+ 3. Movable excitations. It needs to be possible to combine bulk errors
+ to form simpler errors with one or two symptoms that can be used to
+ move or destroy excitations. If bulk errors don't have this
+ property, decoding will fail when attempting to lift a solution
+ from the matcher requires dragging an excitation along a path but
+ there's no way to move the excitation along that path.
+ 4. Matchable-avoids-color. In parts of the dem that correspond to a
+ matchable code, at least one of the colors must be avoided.
+ Otherwise the matcher may be given a problem that can be solved
+ locally, but when lifting it needs to be solved non-locally.
+
+ Returns:
+ A decoder object that can be used to predict observable flips from
+ detection event samples.
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+
+ >>> dem = stim.Circuit('''
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5
+ ... DETECTOR(0, 0, 0, 1) rec[-4]
+ ... DETECTOR(1, 0, 0, 2) rec[-3]
+ ... DETECTOR(2, 0, 0, 0) rec[-2]
+ ... DETECTOR(3, 0, 0, 1) rec[-1]
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''').detector_error_model()
+
+ >>> decoder = chromobius.CompiledDecoder.from_dem(dem)
+ """
+```
+
+
+```python
+# chromobius.CompiledDecoder.predict_obs_flips_from_dets_bit_packed
+
+# (in class chromobius.CompiledDecoder)
+@staticmethod
+def predict_obs_flips_from_dets_bit_packed(
+ dets: np.ndarray,
+) -> np.ndarray:
+ """Predicts observable flips from detection events.
+
+ Args:
+ dets: A bit packed numpy array of detection event data. The array can either
+ be 1-dimensional (a single shot to decode) or 2-dimensional (multiple
+ shots to decode, with the first axis being the shot axis and the second
+ axis being the detection event byte axis).
+
+ The array's dtype must be np.uint8. If you have an array of dtype
+ np.bool_, you have data that's not bit packed. You can pack it by
+ using `np.packbits(array, bitorder='little')`. But ideally you
+ should attempt to never have unpacked data in the first place,
+ since it's 8x larger which can be a large performance loss. For
+ example, stim's sampler methods all have a `bit_packed=True` argument
+ that cause them to return bit packed data.
+
+ Returns:
+ A bit packed numpy array of observable flip data. The array will have
+ the same number of dimensions as the dets argument.
+
+ If dets is a 1D array, then the result has:
+ shape = (math.ceil(num_obs / 8),)
+ dtype = np.uint8
+ If dets is a 2D array, then the result has:
+ shape = (dets.shape[0], math.ceil(num_obs / 8),)
+ dtype = np.uint8
+
+ To determine if the observable with index k was flipped in shot s, compute:
+ `bool((result[s, k // 8] >> (k % 8)) & 1)`
+
+ Example:
+ >>> import stim
+ >>> import chromobius
+ >>> import numpy as np
+
+ >>> repetition_color_code = stim.Circuit('''
+ ... # Apply noise.
+ ... X_ERROR(0.1) 0 1 2 3 4 5 6 7
+ ... # Measure three-body stabilizers to catch errors.
+ ... MPP Z0*Z1*Z2 Z1*Z2*Z3 Z2*Z3*Z4 Z3*Z4*Z5 Z4*Z5*Z6 Z5*Z6*Z7
+ ...
+ ... # Annotate detectors, with a coloring in the 4th coordinate.
+ ... DETECTOR(0, 0, 0, 2) rec[-6]
+ ... DETECTOR(1, 0, 0, 0) rec[-5]
+ ... DETECTOR(2, 0, 0, 1) rec[-4]
+ ... DETECTOR(3, 0, 0, 2) rec[-3]
+ ... DETECTOR(4, 0, 0, 0) rec[-2]
+ ... DETECTOR(5, 0, 0, 1) rec[-1]
+ ...
+ ... # Check on the message.
+ ... M 0
+ ... OBSERVABLE_INCLUDE(0) rec[-1]
+ ... ''')
+
+ >>> # Sample the circuit.
+ >>> shots = 4096
+ >>> sampler = repetition_color_code.compile_detector_sampler()
+ >>> dets, actual_obs_flips = sampler.sample(
+ ... shots=shots,
+ ... separate_observables=True,
+ ... bit_packed=True,
+ ... )
+
+ >>> # Decode with Chromobius.
+ >>> dem = repetition_color_code.detector_error_model()
+ >>> decoder = chromobius.compile_decoder_for_dem(dem)
+ >>> predicted_flips = decoder.predict_obs_flips_from_dets_bit_packed(dets)
+
+ >>> # Count mistakes.
+ >>> differences = np.any(predicted_flips != actual_obs_flips, axis=1)
+ >>> mistakes = np.count_nonzero(differences)
+ >>> assert mistakes < shots / 5
+ """
+```
diff --git a/doc/developers.md b/doc/developers.md
new file mode 100644
index 0000000..9b93efe
--- /dev/null
+++ b/doc/developers.md
@@ -0,0 +1,214 @@
+# Chromobius Developer Documentation
+
+## Index
+
+- [Repository Layout](#Repository_Layout)
+- [Building chromobius as a standalone command line tool](#build-cli)
+ - [with bazel](#build-cli-bazel)
+ - [with cmake](#build-cli-cmake)
+ - [with gcc](#build-cli-gcc)
+ - [with clang](#build-cli-clang)
+- [Building chromobius as a python package](#build-python)
+ - [with bazel](#build-python-bazel)
+ - [with cmake](#build-python-cmake)
+ - [with cibuildwheels](#build-python-cibuildwheels)
+ - [with pip](#build-python-pip)
+- [Running tests](#test)
+ - [python unit tests](#test-python)
+ - [C++ unit tests with Bazel](#test-bazel)
+ - [C++ unit tests with cmake](#test-cmake)
+- [Running performance benchmarks](#perf)
+ - [with bazel](#perf-bazel)
+ - [with cmake](#perf-cmake)
+
+
+## Repository Layout
+
+- `src/chromobius/`: C++ code implementing Chromobius and its python package.
+- `src/clorco/`: Python code for generating color code and surface code circuits used to test Chromobius.
+- `src/gen/`: Generic utilities for making circuits; used by `src/clorco/`.
+- `tools/`: Bash scripts for generating circuits and statistics and plots presented in the paper.
+
+
+## Building chromobius as a standalone command line tool
+
+
+### with bazel:
+
+```bash
+bazel build :chromobius
+```
+
+Or, to run the tool:
+
+```bash
+bazel run :chromobius
+```
+
+
+### with cmake:
+
+```bash
+cmake .
+make chromobius_pybind
+```
+
+Then, to run the built tool:
+
+```bash
+out/chromobius
+```
+
+
+### with gcc:
+
+```bash
+# This must be run from the repository root.
+# This requires that you have libstim and libpymatching installed.
+
+readarray -d '' CC_FILES_TO_BUILD < \
+ <( \
+ find src \
+ | grep "\\.cc$" \
+ | grep -v "\\.\(test\|perf\|pybind\)\\.cc$" \
+ )
+
+g++ \
+ -I src \
+ -std=c++20 \
+ -O3 \
+ -march=native \
+ ${CC_FILES_TO_BUILD[@]} \
+ -l stim \
+ -l pymatching
+```
+
+
+### with clang:
+
+```bash
+# This must be run from the repository root.
+# This requires that you have libstim and libpymatching installed.
+
+readarray -d '' CC_FILES_TO_BUILD < \
+ <( \
+ find src \
+ | grep "\\.cc$" \
+ | grep -v "\\.\(test\|perf\|pybind\)\\.cc$" \
+ )
+
+clang \
+ -I src \
+ -std=c++20 \
+ -O3 \
+ -march=native \
+ ${CC_FILES_TO_BUILD[@]} \
+ -l "stdc++" \
+ -l m \
+ -l stim \
+ -l pymatching
+```
+
+Then, to run the built tool:
+
+```bash
+./a.out
+```
+
+
+## Building chromobius as a python package
+
+
+### with bazel:
+
+```bash
+bazel build :chromobius_dev_wheel
+pip install bazel-bin/chromobius-0.0.dev0-py3-none-any.whl
+```
+
+
+### with cmake:
+
+```bash
+# Requires pybind11 and python to be installed on your system.
+cmake .
+make chromobius_pybind
+# output is in `out/` with a path that depends on your machine
+# e.g. it might be `out/chromobius.cpython-311-x86_64-linux-gnu.so`
+```
+
+
+### with cibuildwheel:
+
+```bash
+pip install cibuildwheel
+# See https://cibuildwheel.readthedocs.io/en/stable/options/#build-skip for CIBW_BUILD values
+CIBW_BUILD=cp311-manylinux_x86_64 cibuildwheel --platform linux
+# output is in `wheelhouse/` with a path that depends on platform/target
+# e.g. it might be `out/chromobius-0.0.dev0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl`
+```
+
+
+### with pip:
+
+```bash
+# Must be run from the chromobius git repository root.
+# Note this will build the package AND install it into your python environment.
+pip install .
+# output is in `python_build_chromobius/` under a platform dependent directory and filename
+# e.g. it might be `python_build_chromobius/lib.linux-x86_64-cpython-311/chromobius.cpython-311-x86_64-linux-gnu.so`
+```
+
+
+
+## Running unit tests
+
+
+### python unit tests
+
+The python unit tests check that the circuit generation utilities
+are working correctly, and that Chromobius can decode the generated
+circuits.
+
+Note that these tests require the chromobius python package to be installed.
+
+```bash
+pip install -r requirements.txt
+pytest src
+```
+
+
+### C++ unit tests with bazel
+
+```bash
+bazel test :all
+```
+
+
+### C++ unit tests with cmake
+
+```bash
+# Requires googletest to be installed on your system.
+cmake .
+make chromobius_test
+out/chromobius_test
+```
+
+
+## Running performance benchmarks
+
+
+### with bazel
+
+```bash
+bazel run :chromobius_perf
+```
+
+
+### with cmake
+
+```bash
+cmake .
+make chromobius_perf
+out/chromobius_perf
+```
diff --git a/doc/getting_started.ipynb b/doc/getting_started.ipynb
new file mode 100644
index 0000000..f9964f8
--- /dev/null
+++ b/doc/getting_started.ipynb
@@ -0,0 +1,2694 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "eddcd418-aac7-41bf-806a-abf24775e2d5",
+ "metadata": {},
+ "source": [
+ "# Getting Started with Chromobius\n",
+ "\n",
+ "1. [What is Chromobius?](#what-is-chromobius)\n",
+ "2. [Installing Chromobius](#installing)\n",
+ "3. [Creating a Color Code Circuit](#circuit)\n",
+ "4. [Decoding shots from your circuit with Chromobius](#decode)\n",
+ "5. [Using Sinter to collect and plot statistics](#sinter)\n",
+ "\n",
+ "# Prereqs\n",
+ "This tutorial assumes you can read and write Python code, and that you have a working **Python 3.10**+ environment.\n",
+ "\n",
+ "This tutorial assumes you're somewhat familiar with quantum error correction, especially stabilizer codes, and *especially* the color code. It assumes you know what a color code is and why you'd want to decode it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c2c8e32-b8a5-408c-aa80-4c803946e192",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "\n",
+ "# What is Chromobius?\n",
+ "\n",
+ "Chromobius is an [open source](https://github.com/quantumlib/chromobius) color code decoder.\n",
+ "Chromobius is an implementation of the [mobius decoder](https://arxiv.org/abs/2108.11395), which approximates the color code decoding problem as a minimum weight matching problem.\n",
+ "Chromobius uses [PyMatching](https://github.com/oscarhiggott/PyMatching/) to solve the minimum weight matching problem."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4435295-281d-438e-bd92-87d86c4a1adb",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# Installing Chromobius\n",
+ "\n",
+ "Chromobius is available as a python pypi package.\n",
+ "You can install Chromobius with `pip` by running the command `pip install chromobius`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c99723c9-56c3-4123-8935-4a3868b2dcb3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install chromobius~=1.0.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6ae556d-e7d7-435b-b041-8fffa65f9836",
+ "metadata": {},
+ "source": [
+ "You can check that you installed Chromobius by importing it and printing its version:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0c74f8ae-5ea1-424b-a57e-3d8bebe12b68",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.0.dev0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import chromobius\n",
+ "print(chromobius.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c89a941-5b7b-4f64-91f4-620af1f9ee89",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# Creating a Color Code Circuit\n",
+ "\n",
+ "The first (and unfortunately hardest) part of benchmarking a QEC circuit is creating the circuit.\n",
+ "For this notebook, you'll create a color code circuit with a phenomenological noise model.\n",
+ "\n",
+ "A color code is typically built out of a hexagonal tiling, with the hexagons truncated into trapezoids at boundaries forming an overall triangular shape.\n",
+ "You don't want to be dealing with trapezoids-vs-hexagons logic when also dealing with circuit logic.\n",
+ "So, the first thing you should do is to build a list of objects describing the tiling.\n",
+ "\n",
+ "Define a `Tile` class with a list of qubit positions, as well as a color.\n",
+ "The positions are 2D locations, which can be conveniently represented in Python using complex numbers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bcd2bd06-ac3b-49cb-987b-4bf7e48e8816",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dataclasses\n",
+ "\n",
+ "@dataclasses.dataclass\n",
+ "class Tile:\n",
+ " qubits: list[complex]\n",
+ " color: str"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20345285-8343-49d4-95e1-505b39f41139",
+ "metadata": {},
+ "source": [
+ "You can make the tiling for a color code by starting with a normal hexagonal tiling, and then cutting away anything outside three half-planes defining the boundaries.\n",
+ "To avoid issues with floating point rounding error, it's safest to use a distorted hexagonal grid where all vertices have integer coordinates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c349d77a-87a5-45cb-b520-cc9483a0d00e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_color_code_tiles(*, base_data_width: int) -> list[Tile]:\n",
+ " if not (base_data_width % 2 == 1 and base_data_width >= 3):\n",
+ " raise ValueError(f\"{base_data_width=} wasn't an odd number at least as large as 3.\")\n",
+ " w = base_data_width * 2 - 1\n",
+ "\n",
+ " def is_in_bounds(q: complex) -> bool:\n",
+ " # Check that it's within the intersection of the three boundary half planes.\n",
+ " if q.imag < 0:\n",
+ " # Too far up.\n",
+ " return False\n",
+ " if q.imag * 2 > q.real * 3:\n",
+ " # Too far downleft.\n",
+ " return False\n",
+ " if q.imag * 2 > (w - q.real) * 3:\n",
+ " # Too far downright.\n",
+ " return False\n",
+ " return True\n",
+ "\n",
+ " # Make a hexagonal tiling, cutting away any vertices that are out of bounds.\n",
+ " tiles = []\n",
+ " hexagon_offsets = [-1, +1j, +1j + 1, +2, -1j + 1, -1j]\n",
+ " for x in range(1, w, 2):\n",
+ " for y in range((x // 2) % 2, w, 2):\n",
+ " q = x + 1j * y\n",
+ " tile = Tile(\n",
+ " color=['red', 'green', 'blue'][y % 3],\n",
+ " qubits=[\n",
+ " q + d\n",
+ " for d in hexagon_offsets\n",
+ " if is_in_bounds(q + d)\n",
+ " ],\n",
+ " )\n",
+ " # Only keep the hexagons and trapezoids.\n",
+ " # No empty tiles, or single-point tiles, or etc.\n",
+ " if len(tile.qubits) in [4, 6]:\n",
+ " tiles.append(tile)\n",
+ "\n",
+ " return tiles "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d7edf94-a405-4b56-ab1c-5c18ba8cc20e",
+ "metadata": {},
+ "source": [
+ "The easiest way to check that the tiling is correct is to draw it.\n",
+ "The easiest way to do draw it is by creating an SVG image.\n",
+ "An SVG image is easy to show in a python notebook, because any class with a `_repr_svg_` method will display as an image.\n",
+ "And an SVG image is reasonably easy to create, because it's just a list of [drawing commands written in an HTML style](https://developer.mozilla.org/en-US/docs/Web/SVG/Tutorial/Paths).\n",
+ "For example, the command `` will draw a red square with a black outline.\n",
+ "\n",
+ "By turning each tile into a path command, you can transform tiles into shapes in an SVG image:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "2379f28a-f09a-45d0-827e-19b4317bf817",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class ShowAsSvg:\n",
+ " def __init__(self, svg: str):\n",
+ " self.svg = svg\n",
+ " def _repr_svg_(self) -> str:\n",
+ " return self.svg\n",
+ "\n",
+ "def draw_tiles_as_svg(tiles: list[Tile]) -> ShowAsSvg:\n",
+ " # Size the SVG to fit the tiles.\n",
+ " max_r = max(q.real for tile in tiles for q in tile.qubits)\n",
+ " max_i = max(q.imag for tile in tiles for q in tile.qubits)\n",
+ " svg = f''\n",
+ " \n",
+ " return ShowAsSvg(svg)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c8f4da8-6e13-4fa9-88f0-e6986356e252",
+ "metadata": {},
+ "source": [
+ "Now that you have this method, you can use it on your color code tiling method to get a picture of a color code!\n",
+ "It will look a bit wobbly because you distorted the vertices to land on integer coordinates, but that's fine:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e851d72f-6865-48f4-af51-0d82a642c0e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ "<__main__.ShowAsSvg at 0x7f77347dacd0>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "draw_tiles_as_svg(make_color_code_tiles(base_data_width=13))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "670a7411-d291-4bca-85cc-ed605d5ff915",
+ "metadata": {},
+ "source": [
+ "Now that you have the building blocks of a color code, you can go about turning them into a circuit.\n",
+ "You first need to prepare the system by measuring the observables and stabilizers without noise.\n",
+ "Then you need to perform a specified number of rounds of stabilizer measurement with noise.\n",
+ "Finally, you need to end the circuit with one last noiseless measurement of the stabilizers and the observables.\n",
+ "\n",
+ "For each stabilizer measurement, you need to declare a `DETECTOR` comparing it to the corresponding measurement from the previous round.\n",
+ "The easiest way to do this is to make sure you always measure the stabilizers in the same order, so that the offset between measurements to compare is always the same.\n",
+ "For the observable measurements, the measurement indexing logic is much easier because you can lean on the fact that stim's `OBSERVABLE_INCLUDE` instruction can gradually accumulate measurements for an observable over the entire circuit.\n",
+ "(The `DETECTOR` instruction instead requires all measurements to be given at once).\n",
+ "\n",
+ "Other details you want to include are: `QUBIT_COORDS` instructions indicating the spatial layout, `TICK` instructions indicating the progression of time, and noise instructions indicating qubits to depolarize and measurements to probabilistically flip.\n",
+ "(For more complicated circuits, you would try to split this work into more pieces.\n",
+ "For example, you would add noise to the finished circuit, instead of adding it while building the circuit.)\n",
+ "\n",
+ "A very key detail that you must include in this circuit, that wouldn't normally be required by a stim circuit, is annotations on every detector stating its basis and color.\n",
+ "Chromobius needs this information in order to decode the circuit.\n",
+ "You add color and basis annotations to detectors by using their coordinate data.\n",
+ "Chromobius will look at the 4th coordinate of a detector's coordinate data to see what its basis and color are, using this convention:\n",
+ "- 0: red X\n",
+ "- 1: green X\n",
+ "- 2: blue X\n",
+ "- 3: red Z\n",
+ "- 4: green Z\n",
+ "- 5: blue Z.\n",
+ "\n",
+ "As you can maybe tell, making a circuit can be a very involved process!\n",
+ "This notebook isn't really about making circuits, and you probably don't want to get too bogged down in this detail, so let's not get stuck on this.\n",
+ "Just... write a function that makes an annotated color code circuit:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "5887447a-4720-4f68-b99b-cf1a22cd9e5f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import stim\n",
+ "from typing import Literal\n",
+ "\n",
+ "def make_color_code_circuit(\n",
+ " *,\n",
+ " obs_basis: Literal['X', 'Y', 'Z'],\n",
+ " base_data_width: int,\n",
+ " rounds: int,\n",
+ " noise_strength: float,\n",
+ ") -> stim.Circuit:\n",
+ " \"\"\"Creates a color code circuit with a phenomenological noise model.\n",
+ "\n",
+ " The circuit's detectors are annotated so that Chromobius can decode them.\n",
+ "\n",
+ " Args:\n",
+ " obs_basis: The basis of the observable to prepare and verify at the end of the circuit.\n",
+ " base_data_width: The number of data qubits along one side of the patch.\n",
+ " rounds: The number of times to apply depolarizing noise. One more than the number of\n",
+ " times to apply measurement noise.\n",
+ " noise_strength: The strength of the depolarizing noise applied to the data qubits,\n",
+ " and also the probability of noisy measurements reporting the wrong result.\n",
+ "\n",
+ " Returns:\n",
+ " The created circuit.\n",
+ " \"\"\"\n",
+ "\n",
+ " def mpp_targets(\n",
+ " qubits: list[complex],\n",
+ " basis: Literal['X', 'Y', 'Z']\n",
+ " ) -> list[stim.GateTarget]:\n",
+ " \"\"\"Makes a pauli product for an MPP instruction.\"\"\"\n",
+ " target_b = {'X': stim.target_x, 'Y': stim.target_y, 'Z': stim.target_z}[basis]\n",
+ " indices = sorted(q2i[q] for q in qubits)\n",
+ " targets = []\n",
+ " for k in indices:\n",
+ " targets.append(target_b(k))\n",
+ " targets.append(stim.target_combiner())\n",
+ " targets.pop()\n",
+ " return targets\n",
+ "\n",
+ " def measure_observables() -> stim.Circuit:\n",
+ " \"\"\"Make instructions to measure an observable of the color code.\"\"\"\n",
+ " c = stim.Circuit()\n",
+ " c.append(\"MPP\", mpp_targets(sorted_qubits, obs_basis))\n",
+ " c.append(\"OBSERVABLE_INCLUDE\", stim.target_rec(-1), 0)\n",
+ " return c\n",
+ " \n",
+ " def measure_stabilizers(\n",
+ " *,\n",
+ " data_noise_after: bool,\n",
+ " measure_noise: bool,\n",
+ " include_detectors: bool,\n",
+ " ) -> stim.Circuit:\n",
+ " \"\"\"Make instructions to measure the stabilizers of the color code.\"\"\"\n",
+ " c = stim.Circuit()\n",
+ "\n",
+ " # Measure every stabilizer.\n",
+ " for basis in ['X', 'Z']:\n",
+ " for tile in tiles:\n",
+ " c.append(\"MPP\", mpp_targets(tile.qubits, basis), noise_strength if measure_noise else None)\n",
+ " \n",
+ " # Compare the measurements to the previous round to produce detection events.\n",
+ " if include_detectors:\n",
+ " num_stabilizers = len(tiles) * 2\n",
+ " for xz in range(2):\n",
+ " for k, tile in enumerate(tiles):\n",
+ " center = sum(tile.qubits) / len(tile.qubits)\n",
+ " chromobius_color = {'red': 0, 'green': 1, 'blue': 2}[tile.color]\n",
+ " chromobius_annotation = chromobius_color + xz*3\n",
+ " offset = xz * len(tiles) + k\n",
+ " c.append(\"DETECTOR\", [\n",
+ " stim.target_rec(-num_stabilizers + offset), \n",
+ " stim.target_rec(-num_stabilizers*2 + offset),\n",
+ " ], [center.real, center.imag, 0, chromobius_annotation])\n",
+ "\n",
+ " # End the round.\n",
+ " if data_noise_after:\n",
+ " c.append(\"DEPOLARIZE1\", range(len(all_qubits)), noise_strength)\n",
+ " c.append(\"SHIFT_COORDS\", [], [0, 0, 1])\n",
+ " c.append(\"TICK\")\n",
+ "\n",
+ " return c\n",
+ "\n",
+ " tiles = make_color_code_tiles(base_data_width=base_data_width)\n",
+ " circuit = stim.Circuit()\n",
+ "\n",
+ " # Index the qubit coordinates and put coordinate data in the circuit.\n",
+ " all_qubits = {q for tile in tiles for q in tile.qubits}\n",
+ " sorted_qubits = sorted(all_qubits, key=lambda q: (q.real, q.imag))\n",
+ " q2i = {q: i for i, q in enumerate(sorted_qubits)}\n",
+ " for q, i in q2i.items():\n",
+ " circuit.append(\"QUBIT_COORDS\", [i], [q.real, q.imag])\n",
+ "\n",
+ " # Use the helper methods you just defined to build the rounds and combine them into a full circuit.\n",
+ " circuit += measure_observables()\n",
+ " circuit += measure_stabilizers(data_noise_after=True, measure_noise=False, include_detectors=False)\n",
+ " circuit += (rounds - 1) * measure_stabilizers(data_noise_after=True, measure_noise=True, include_detectors=True)\n",
+ " circuit += measure_stabilizers(data_noise_after=False, measure_noise=False, include_detectors=True)\n",
+ " circuit += measure_observables()\n",
+ " \n",
+ " return circuit"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aaa82ea6-77d6-4f0f-9597-5004e942c07c",
+ "metadata": {},
+ "source": [
+ "To look at the circuit, you can make a timeline diagram:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f9d25d3e-9242-41c1-bf9f-e2b329d56626",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "make_color_code_circuit(\n",
+ " obs_basis='Y',\n",
+ " base_data_width=3,\n",
+ " rounds=7,\n",
+ " noise_strength=1e-3,\n",
+ ").diagram(\"timeline\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0b25005-68b5-436f-a871-9fd4e03d4091",
+ "metadata": {},
+ "source": [
+ "You can also make a \"detslice\" diagram, which shows stabilizers of the circuit from moment to moment.\n",
+ "It's called \"det\" slice because the stabilizers that are shown correspond to the sensitivity regions of the detectors declared by the circuit.\n",
+ "For a color code, this means the detslice diagram looks like a color code.\n",
+ "(The shapes will all have the same color, instead of a three coloring, because in the diagram the color indicates the basis being checked and all the shapes use the same basis)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "4d1bb1c3-7fae-423f-8fc6-0e58363863bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "make_color_code_circuit(\n",
+ " obs_basis='Y',\n",
+ " base_data_width=3,\n",
+ " rounds=7,\n",
+ " noise_strength=1e-3,\n",
+ ").diagram(\"detslice\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cff2307-35bb-4276-9997-3c394fe953eb",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# Decoding shots from your circuit with Chromobius\n",
+ "\n",
+ "Now that you can make color code circuits, you can make a circuit to sample and decode shots from."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "68c20917-6f02-46c9-997e-873c9b27a604",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "circuit = make_color_code_circuit(\n",
+ " obs_basis='X',\n",
+ " base_data_width=7,\n",
+ " rounds=5,\n",
+ " noise_strength=1e-2,\n",
+ ")\n",
+ "sampler = circuit.compile_detector_sampler()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c789935-4f12-413a-b6cc-5fcb230295e9",
+ "metadata": {},
+ "source": [
+ "To configure Chromobius, you need the detector error model (the \"dem\") of the circuit.\n",
+ "A dem is a representation of the circuit's \"[Tanner graph](https://en.wikipedia.org/wiki/Tanner_graph)\", which describes all error mechanisms and their symptoms.\n",
+ "This abstracts away all of the circuit-y details that a decoder doesn't want to have to care about.\n",
+ "\n",
+ "You can get the circuit's dem with `circuit.detector_error_model()`.\n",
+ "You can make diagrams of dems using their diagram method.\n",
+ "For example, the `matchgraph-svg` diagram is a picture of the decoding graph.\n",
+ "\n",
+ "Each node in a matchgraph diagram is a detector, and each edge is an error.\n",
+ "The color code decoding graph is interesting in that it has hyper edges; errors with three symptoms instead of one or two.\n",
+ "In the diagram these are shown by blue lines converging to a common midpoint from all three symptoms.\n",
+ "Since the circuit has multiple rounds, the diagram shows multiple layers of nodes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "dc7cc10b-1817-49e9-abed-21040afdb146",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dem = circuit.detector_error_model()\n",
+ "dem.diagram(\"matchgraph-svg\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bd4f391-397d-4ce4-a97d-a463297118f0",
+ "metadata": {},
+ "source": [
+ "You can also get a 3d model of the graph using the `matchgraph-3d` diagram type.\n",
+ "(If you're viewing this notebook on github, the 3d viewer won't show up.\n",
+ "If it does show up, you can click and drag to move the view around.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "bcc02e51-d0f7-43c2-ad1e-27f89409f2bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ 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+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dem.diagram(\"matchgraph-3d\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5460fabd-195c-4f5c-a12e-3146404637b8",
+ "metadata": {},
+ "source": [
+ "Anyways, putting the cool 3d model viewer aside, now that you have the dem you can use it to configure Chromobius.\n",
+ "You do this by calling `chromobius.compile_decoder_for_dem`, like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e822b618-81b7-4d9e-b062-5740d57a3a7f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "decoder = chromobius.compile_decoder_for_dem(dem)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1cc5417-681f-4105-8964-66fa7d558dd0",
+ "metadata": {},
+ "source": [
+ "With the decoder in hand, you can take detector shots from the circuit and have Chromobius predict whether or not the logical observable was flipped.\n",
+ "The method for doing this is `predict_obs_flips_from_dets_bit_packed`.\n",
+ "The `bit_packed` means each byte of data given to it must contain 8 bits of data, instead of only 1 bit per byte.\n",
+ "You can get bit packed detection events from stim samplers by passing `bit_packed=True`.\n",
+ "If you have unbitpacked data, you can pack it by using [`np.packbits(data, bitorder='little')`](https://numpy.org/doc/stable/reference/generated/numpy.packbits.html)\n",
+ "\n",
+ "After Chromobius makes its predictions, you can compare them to the actual observable flips and count how many times Chromobius made a mistake:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "98e3f2b1-7521-4b88-ae0a-95978152abef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mistakes=0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "dets, actual_obs = sampler.sample(\n",
+ " shots=1024,\n",
+ " separate_observables=True,\n",
+ " bit_packed=True,\n",
+ ")\n",
+ "\n",
+ "predicted_obs = decoder.predict_obs_flips_from_dets_bit_packed(dets)\n",
+ "\n",
+ "mistakes = np.count_nonzero(np.any(predicted_obs != actual_obs, axis=1))\n",
+ "print(f'{mistakes=}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee7500f3-0a07-479f-be0f-1b7451302d02",
+ "metadata": {},
+ "source": [
+ "1024 shots isn't really enough to get a sense of the error rate for this circuit.\n",
+ "It's possible you didn't even see one mistake.\n",
+ "Try taking a hundred times as many shots:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "8feab0c7-cba5-4030-89fb-cc5b6cd08394",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " shots = 102400\n",
+ " mistakes = 28\n",
+ " error_rate = 0.027344%\n",
+ "shots/second = 89627\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "t0 = time.monotonic()\n",
+ "total_shots = 0\n",
+ "total_mistakes = 0\n",
+ "for _ in range(100):\n",
+ " total_shots += 1024\n",
+ " dets, actual_obs = sampler.sample(\n",
+ " shots=1024,\n",
+ " separate_observables=True,\n",
+ " bit_packed=True,\n",
+ " )\n",
+ " predicted_obs = decoder.predict_obs_flips_from_dets_bit_packed(dets)\n",
+ " total_mistakes += np.count_nonzero(np.any(predicted_obs != actual_obs, axis=1))\n",
+ "t1 = time.monotonic()\n",
+ "\n",
+ "print(f' shots = {total_shots}')\n",
+ "print(f' mistakes = {total_mistakes}')\n",
+ "print(f' error_rate = {total_mistakes / total_shots:%}')\n",
+ "print(f'shots/second = {round(total_shots / (t1 - t0))}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92965b1e-abf4-4e59-a163-9df20e7811c0",
+ "metadata": {},
+ "source": [
+ "Nice! Pretty quick, right? But you probably don't want to have to deal with all this batching and collecting and timing stuff (nevermind multiprocessing!). That's what `sinter` is for."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cebf42c7-7b8d-4c54-bd98-a0637a2ef407",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# Using Sinter to collect and plot statistics\n",
+ "\n",
+ "Sinter is a tool for performing bulk Monte Carlo sampling of quantum error correction circuits using python multiprocessing.\n",
+ "Like Chromobius, you can install `sinter` using `pip`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "4f62876b-cefa-4dc1-bc83-f6e0df111cfa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install sinter~=1.12 --quiet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58a16293-eceb-426d-bbfa-2e98cf4712b9",
+ "metadata": {},
+ "source": [
+ "To get sinter to sample and decode circuits, you need to wrap the circuits into `sinter.Task`s that provide metadata about the circuits.\n",
+ "This metadata will be useful later, when plotting.\n",
+ "Typically you want to explore a variety of circuit sizes and noise strengths.\n",
+ "Try defining a list of sinter tasks for a variety of sizes and noise strengths:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "e0e7d670-4461-463b-80be-270f3766669f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sinter\n",
+ "\n",
+ "tasks = [\n",
+ " sinter.Task(\n",
+ " circuit=make_color_code_circuit(\n",
+ " obs_basis=b,\n",
+ " base_data_width=d,\n",
+ " rounds=r,\n",
+ " noise_strength=p,\n",
+ " ),\n",
+ " json_metadata={'d': d, 'p': p, 'r': r, 'b': b},\n",
+ " )\n",
+ " for d in [3, 5, 7, 9]\n",
+ " for r in [d*4]\n",
+ " for b in ['X']\n",
+ " for p in [0.01, 0.02]\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "521f7f3e-b816-4633-b103-79cdb325d7fe",
+ "metadata": {},
+ "source": [
+ "Once you have your tasks, you call `sinter.collect` on them to perform sampling.\n",
+ "You also need to specify how many shots to take from each task, how many errors need to be seen before sampling can stop early, how many worker processes to use, and what decoders to use.\n",
+ "You want to use chromobius, so you should set the decoders to `['chromobius']`.\n",
+ "Because chromobius isn't one of sinter's built in decoders, you also have to tell it what `'chomobius'` means.\n",
+ "You do that by giving a dictionary to the `custom_decoders` argument mapping strings to instances of `sinter.Decoder`.\n",
+ "For convenience, the `chromobius.sinter_decoders()` method returns this dictionary.\n",
+ "\n",
+ "You can specify `print_progress=True` to get progress updates.\n",
+ "Collecting these stats should take less than a minute."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4db774f3-638b-4520-b3d5-5ef0453455f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[31mStarting 12 workers...\u001b[0m\n",
+ "\u001b[31m4 cases left:\n",
+ " chromobius processes=5 ~core_mins_left=1 shots_left=95200 errors_left=80 {b=X,d=7,p=0.01,r=28}\n",
+ " chromobius processes=1 ~core_mins_left=1 shots_left=97452 errors_left=27 {b=X,d=7,p=0.02,r=28}\n",
+ " chromobius processes=4 ~core_mins_left=2 shots_left=99400 errors_left=100 {b=X,d=9,p=0.01,r=36}\n",
+ " chromobius processes=2 ~core_mins_left=4 shots_left=99900 errors_left=100 {b=X,d=9,p=0.02,r=36}\u001b[0m\n",
+ "\u001b[31m3 cases left:\n",
+ " chromobius processes=2 ~core_mins_left=1 shots_left=68299 errors_left=15 {b=X,d=7,p=0.01,r=28}\n",
+ " chromobius processes=5 ~core_mins_left=1 shots_left=95200 errors_left=100 {b=X,d=9,p=0.01,r=36}\n",
+ " chromobius processes=3 ~core_mins_left=1 shots_left=99400 errors_left=91 {b=X,d=9,p=0.02,r=36}\u001b[0m\n",
+ "\u001b[31m2 cases left:\n",
+ " chromobius processes=5 ~core_mins_left=1 shots_left=89200 errors_left=96 {b=X,d=9,p=0.01,r=36}\n",
+ " chromobius processes=1 ~core_mins_left=1 shots_left=95319 errors_left=34 {b=X,d=9,p=0.02,r=36}\u001b[0m\n",
+ "\u001b[31m2 cases left:\n",
+ " chromobius processes=5 ~core_mins_left=1 shots_left=78400 errors_left=94 {b=X,d=9,p=0.01,r=36}\n",
+ " chromobius processes=1 ~core_mins_left=1 shots_left=95319 errors_left=34 {b=X,d=9,p=0.02,r=36}\u001b[0m\n",
+ "\u001b[31m1 cases left:\n",
+ " chromobius processes=3 ~core_mins_left=1 shots_left=67200 errors_left=89 {b=X,d=9,p=0.01,r=36}\u001b[0m\n",
+ "\u001b[31m1 cases left:\n",
+ " chromobius processes=2 ~core_mins_left=1 shots_left=52800 errors_left=80 {b=X,d=9,p=0.01,r=36}\u001b[0m\n",
+ "\u001b[31m1 cases left:\n",
+ " chromobius processes=1 ~core_mins_left=1 shots_left=31200 errors_left=75 {b=X,d=9,p=0.01,r=36}\u001b[0m\n",
+ "\u001b[31mDone collecting\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "stats = sinter.collect(\n",
+ " tasks=tasks,\n",
+ " num_workers=os.cpu_count(),\n",
+ " max_shots=100_000,\n",
+ " max_errors=100,\n",
+ " print_progress=True,\n",
+ " decoders=['chromobius'],\n",
+ " custom_decoders=chromobius.sinter_decoders(),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "baf78849-79e1-4b40-a5d7-8058712b6ab1",
+ "metadata": {},
+ "source": [
+ "Now you have statistics describing how often each circuit experienced a logical error, when decoded using Chromobius.\n",
+ "To plot these statistics, you can use `matplotlib`.\n",
+ "You can iterate over the list of stats yourself, but it's usually more convenient to use sinter's `sinter.plot_error_rate` helper method.\n",
+ "It will split the statistics into curves according to a grouping function you provide, lay out those curves over the X axis using an X function you provide, and set the Y coordinate to the error rate.\n",
+ "It will also give uncertainty highlights indicating hypotheses with likelihoods near the max likelihood hypothesis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "e7accd35-3cb5-43fb-8d5f-fded2b8ba73f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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