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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) 🎉

Features and Enhancements (QEC) 🎉

Breaking Changes (QEC) 🛠

Bug Fixes (QEC) 🐛

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 https://github.com/NVIDIA/cudaqx/pull/243

Documentation ✏️

Common / Misc

Testing

New Contributors

Full Changelog: https://github.com/NVIDIA/cudaqx/compare/0.3.0...0.4.0

Source: README.md, updated 2025-07-31