0.4.0

The CUDA-QX 0.4.0 release includes a variety of new features in both the Solvers and QEC library. For the Solvers library, a new Generative Quantum Eigensolver implementation is provided. To use this new algorithm, you will need to use pip install cudaq-solvers[gqe] in order to install all the proper dependencies. For the QEC library, a new Tensor Network Decoder is added, and a new API allows users to automatically generate PCMs from noisy CUDA-Q memory circuits. To use this new decoder, you will need to a) use Python >= 3.11, and b) use pip install cudaq-qec[tensor_network_decoder] in order to install all the proper dependencies. Additionally, support for Python 3.13 is added in this release. Many more features are listed below!

Please check out the docs and examples for how to get started using the CUDA-QX libraries!

Note: CUDA-QX 0.4.0 depends on CUDA-Q 0.12.0.

Features and Enhancements (Solvers) 🎉

• Generative Quantum Eigensolver by @melody-ren in #201

Features and Enhancements (QEC) 🎉

• Tensor Network Decoder by @npancotti in #179
• Add check for tn decoder import by @melody-ren in #206
• Add Parity Check Matrix generation and utility functions by @bmhowe23 in #147
• Add opt_results field to decoder_result by @melody-ren in #171
• Optimize convert_vec_soft_to_tensor_hard by 50x by @bmhowe23 in #160

Breaking Changes (QEC) 🛠

• Update QEC interfaces to use spin_op_term by @bmhowe23 in #165

Bug Fixes (QEC) 🐛

• Add fine grained noise injection in a custom qec code by @kvmto in #227

nv-qldpc-decoder Updates (Closed Source)

• Added multiple algorithm configuration updates, including iter_per_check, clip_value, bp_method (now supporting both sum-product and min-sum), scale_factor, and an optional output result logging capability for bp_llr_history.
  • Add API doc update for BP by @melody-ren #243

Documentation ✏️

• Replace PCM generation example with one using new DEM API by @kvmto in #216
• introduction.rst: clean up docs typos by @mitchdz in #163
• Update doc and example to use decoder_result new API by @melody-ren in #175
• Update examples to show how to use decoder_result API by @melody-ren in #184
• [docs] Update circuit-level noise example to use new DEM feature by @bmhowe23 in #199
• Fix typo in Building.md by @melody-ren in #209
• Doc update by @melody-ren in #213
• Tensor network decoder docs & examples by @npancotti in #214
• Update Python READMEs to describe optional features by @bmhowe23 in #236
• small fix in gqe docstring by @caldwellshane in #239
• Update docs and guard for tensor network decoder by @melody-ren in #241

Common / Misc

• Add python 3.13 support by @melody-ren in #222
• [nfc] Be more explicit about extension_point usage by @bmhowe23 in #177
• Add move constructor to heterogeneous_map by @melody-ren in #186
• Remove unnecessary MeasureCounts.h include by @1tnguyen in #188
• Update restrict pointer syntax to allow compilation with clang++ by @bmhowe23 in #205
• Update cudaq ver for project.toml by @melody-ren in #219
• Fix decoder plugin not throwing the missing dependecy err msg issue by @melody-ren in #224
• Update pyproject.toml dependencies for CUDA-Q 0.12 by @bmhowe23 in #228
• Add missing licence header by @melody-ren in #235

Testing

• Workflow updates (incl creating all_libs_release.yaml) by @bmhowe23 in #159
• [test] Skip nv-qldpc-decoder test if no GPUs found by @bmhowe23 in #238
• Add python3.13 to validate_wheels.sh by @melody-ren in #237

New Contributors

• @mitchdz made their first contribution in #163
• @1tnguyen made their first contribution in #188
• @npancotti made their first contribution in #179

Full Changelog: 0.3.0...0.4.0

0.3.0

The CUDA-QX 0.3.0 release includes updates for CUDA-Q breaking changes (spin ops) and notable performance improvements to our nv-qldpc-decoder. Some of these improvements reduce our average OSD-0 processing by >6X, so try it out!

For details on the breaking changes, see the descriptions in in PRs below along with the associated CUDA-Q PRs:

• NVIDIA/cuda-quantum#2710
• NVIDIA/cuda-quantum#2817

Please check out the docs and examples for how to get started using the CUDA-QX libraries!

What's Changed (Solvers)

• UCCSD operator pool correction and integration in adapt simulator by @kvmto in #65
• Update libraries for cudaq::spin_op breaking changes by @bmhowe23 in #134
• Update libraries for Python spin op breaking changes by @bmhowe23 in #152
• Add __version__ info to packages by @bmhowe23 in #154

What's Changed (QEC)

• Update libraries for cudaq::spin_op breaking changes by @bmhowe23 in #134
• Update libraries for Python spin op breaking changes by @bmhowe23 in #152
• Correcting syndromes per shot in circuit-level example by @justinlietz in #136
• Add __version__ info to packages by @bmhowe23 in #154

nv-qldpc-decoder Updates (Closed Source)

• OSD solver ARM performance improvements by @melody-ren
• Add Gaussian elimination to OSD with early exit @melody-ren
• Fix numerical stability bug with BP by @bmhowe23

Documentation

• Update install guide to clarify usage with no GPUs available by @bmhowe23 in #143

Testing

• Add testcases for libs/core to improve the code coverage rate by @caiyunh in #153

New Contributors

• @caiyunh made their first contribution in #153

Note: CUDA-QX 0.3.0 depends on CUDA-Q 0.11.0.

Full Changelog: 0.2.1...0.3.0

0.2.1

What's Changed

This a is a minor patch release to fix 1 important bug and add 1 important set of error checks. Both of these changes are related the QEC decoders.

• Fix bug in nv-qldpc-decoder where exhaustive search OSD was not searching over the correct candidate bitstrings.
• Add error checking before constructing internal structures from Python data by @bmhowe23 #141

Note: CUDA-QX 0.2.1 depends on CUDA-Q 0.10.0 (same as CUDA-QX 0.2.0).

Full Changelog: 0.2.0...0.2.1

0.2.0

This release of the CUDA-QX libraries adds support for arm64 / aarch64 platforms for both the QEC and Solvers libraries.

CUDA-QX is a collection of libraries that build upon the CUDA-Q programming model to enable the rapid development of hybrid quantum-classical application code leveraging state-of-the-art CPUs, GPUs, and QPUs. It provides a collection of C++ libraries and Python packages that enable research, development, and application creation for use cases in quantum error correction and hybrid quantum-classical solvers.

Please check out the docs and examples for how to get started using the CUDA-QX libraries!

Note: CUDA-QX 0.2.0 depends on CUDA-Q 0.10.0.

What's Changed for QEC

This release includes a new high-performance GPU-accelerated QLDPC decoder implementation based on the algorithms described in Decoding Across the Quantum LDPC Code Landscape. This new decoder requires an NVIDIA GPU. Additionally, this release includes performance improvements in the sample_memory_circuit by utilizing CUDA-Q's new "explicit measurements" feature to accelerate collection of noisy syndrome data when using the stim target.

Features and Enhancements 🎉

• Introduce the new nv-qldpc-decoder, a GPU-accelerated decoder of the Belief Propagation and Ordered Statistics Decoding algorithms described in https://arxiv.org/abs/2005.07016.
• Make DecoderResult tuple-like in python by @melody-ren in #16
• Add Surface Code by @justinlietz in #7
• First shot memory by @justinlietz in #61
• Add python binding for decoder APIs by @melody-ren in #68
• Use explicit measurements for memory circuit experiments by @bmhowe23 in #89
• Support vector types for kwargs by @bmhowe23 in #99

Bug Fixes 🐛

• Fix undefined behavior in cudaq::qec::to_parity_matrix by @bmhowe23 in #87

Breaking Changes 🛠

• Change default decoder float type to double by @bmhowe23 in #31
• Update API name: decode_multi to decode_batch by @melody-ren in #102

Documentation Updates ✏️

• Update docs to clarify Python wheels installation requirements by @bmhowe23 in #34
• [docs] Update old reference to steane_lut decoder by @bmhowe23 in #39
• Add an example of loading a decoder .so by @melody-ren in #42
• Update API documentation by @melody-ren in #71
• Add expressive noise model example by @melody-ren in #123
• Use new CUDA-Q noise models in examples by @bmhowe23 in #120
• [docs] Decoder API clarification by @bmhowe23 in #126

Other Changes

• Update core header files to support C++17 as well by @bmhowe23 in #27
• Add CUDA build support to libraries by @bmhowe23 in #32
• Remove CUDA-Q patch that is no longer needed by @bmhowe23 in #48
• Update CI for arm64 build by @bmhowe23 in #54
• Update test CMakeLists.txt for upstream CUDA-Q change by @bmhowe23 in #93
• Update CUDA-Q build patch by @bmhowe23 in #95
• Test examples in workflow by @melody-ren in #112

What's Changed for Solvers

In addition to arm64 / aarch64 support, this release includes a support for the Bravyi-Kitaev transformation.

Features and Enhancements 🎉

• Add a get_operator_pool function in C++ to mirror the Python by @amccaskey in #13
• Add an option to set the tolerance for jordan_wigner by @melody-ren in #23
• Bravyi-Kitaev implementation by @wsttiger in #35
• Support vector types for kwargs by @bmhowe23 in #99

Bug Fixes 🐛

• Fix signed int overflow in uccsd by @annagrin in #64
• Refactoring and debugging of Jordan Wigner transform (Issue #67) by @kvmto in #82

Breaking Changes 🛠

Documentation Updates ✏️

• Update docs to clarify Python wheels installation requirements by @bmhowe23 in #34
• Update API documentation by @melody-ren in #71

Other Changes

• Update core header files to support C++17 as well by @bmhowe23 in #27
• Add CUDA build support to libraries by @bmhowe23 in #32
• Remove CUDA-Q patch that is no longer needed by @bmhowe23 in #48
• Update CI for arm64 build by @bmhowe23 in #54
• Update test CMakeLists.txt for upstream CUDA-Q change by @bmhowe23 in #93
• Update CUDA-Q build patch by @bmhowe23 in #95
• Test examples in workflow by @melody-ren in #112

New Contributors

• @boschmitt made their first contribution in #8
• @amccaskey made their first contribution in #13
• @melody-ren made their first contribution in #16
• @bmhowe23 made their first contribution in #18
• @wsttiger made their first contribution in #35
• @justinlietz made their first contribution in #7
• @annagrin made their first contribution in #64
• @kvmto made their first contribution in #82
• @caldwellshane made their first contribution in #117

Full Changelog: 0.1.0...0.2.0

0.1.0

This is the initial release of the CUDA-QX libraries! CUDA-QX is a collection of libraries that build upon the CUDA-Q programming model to enable the rapid development of hybrid quantum-classical application code leveraging state-of-the-art CPUs, GPUs, and QPUs. It provides a collection of C++ libraries and Python packages that enable research, development, and application creation for use cases in quantum error correction and hybrid quantum-classical solvers. Please check out the docs and examples for how to get started using the CUDA-QX libraries!

Note: CUDA-QX 0.1.0 depends on CUDA-Q 0.9.0.