Changelog#

All notable changes to tn4ml are documented here.

v1.1.0#

Fixed

  • Windows installation. Pinned orbax-checkpoint>=0.11.34 so the transitive uvloop dependency (which has no Windows wheels) is correctly skipped on Windows. orbax-checkpoint==0.11.33 declared uvloop unconditionally, which broke pip install tn4ml on Windows; 0.11.34+ guards it with platform_system != "Windows". The previous pip install --no-deps tn4ml workaround is no longer needed.

  • Fixed mypy errors across the codebase (#39).

Changed

  • Removed the hardcoded softmax from the model — this affects model output and evaluation (#40).

  • Updated the batching function in model.py (#37).

  • Dropped support for Python < 3.10.

  • Renamed the test/ directory to tests/.

  • Updated example notebooks and refined extra dependencies.

Added

  • Developer tooling: pre-commit hooks and CI pre-merge checks (ruff, mypy, bandit), plus automatic version inheritance in the CI/CD pipeline (#37).

v1.0.5#

Changed

  • Refined extras_require in setup.py to ensure correct installation of the example dependencies. Install them with pip install "tn4ml[examples]".

Added

  • New example scripts from the paper “tn4ml: Tensor Network Training and Customization for Machine Learning”.

  • Updated documentation.

v1.0.4#

Fixed

  • Normalization issue for large systems (#28).

  • Corrected canonization in Strategy.Sweeps.

Added

  • device option in Model.configure() (#26).

  • Updated example notebooks.

v1.0.3#

Fixed

  • Model training and evaluation (#19).

  • Validation and model saving issues (#17).

  • metrics.CombinedLoss().

Added

  • New PatchAmplitudeEmbedding embedding (#20, thanks @gabrieledangeli).

  • model.forward() function.

v1.0.2#

Fixed

  • Fix in initializers.py.

  • Fix in model.py — affects model evaluation.

v1.0.1#

Added

  • A few new features for embeddings.

Fixed

  • Bug fixes and performance improvements.

  • Minor issues found in version 1.0.0.

v1.0.0#

Added

  • Initial release of tn4ml: tensor networks for machine learning built on top of Quimb and JAX, with support for 1D tensor network structures (Matrix Product State, Matrix Product Operator, Spaced Matrix Product Operator), embeddings, initializers, objective functions, and optimization strategies.