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Welcome to MentPy’s documentation

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MentPy is in its alpha version and is under active development.

The mentpy library is an open-source Python package for creating and training quantum machine learning (QML) models in the measurement-based quantum computing (MBQC) framework. This library contains functions to automatically calculate the causal flow or generalized flow of a graph and tools to analyze the expressivity of the MBQC ansatzes.

Features

  • Manipulation of graph states.

  • Automatically calculate the causal flow or generalized flow of a graph.

  • Simulate MBQC circuits.

  • Optimize measurement angles in MBQC ansatzes used for QML.

  • Create data and noisy data for training QML models.

  • Determine the lie algebra of an MBQC ansatz.

Roadmap

  • Improve current simulators for MBQC circuits.

  • Increase code coverage.

  • Add autodiff support for MBQC circuits.

  • Add support for more general MBQC states.

  • Integrate with pyzx to optimize resources in MBQC circuits.

Contributing

If you would like to contribute to this project, please feel free to open an issue or pull request 😄.

Acknowledgements

Luis would like to thank his M.Sc. supervisors, Dr. Dmytro Bondarenko, Dr. Polina Feldmann, and Dr. Robert Raussendorf for their guidance during the development of this library.

Citation

If you find MentPy useful in your research, please consider citing us 🙂

@article{mantilla2025mbqml,
  title = {Measurement-based quantum machine learning},
  author = {Mantilla Calder\'on, Luis and Raussendorf, Robert and Feldmann, Polina and Bondarenko, Dmytro},
  journal = {Phys. Rev. A},
  volume = {113},
  number = {4},
  pages = {042421},
  year = {2026},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/2snk-m8c6},
  url = {https://link.aps.org/doi/10.1103/2snk-m8c6}
}

L. Mantilla Calderón, R. Raussendorf, P. Feldmann, and D. Bondarenko, Measurement-based quantum machine learning, Phys. Rev. A 113, 042421 (2026). https://doi.org/10.1103/2snk-m8c6

Mantilla Calderón, L., Raussendorf, R., Feldmann, P., & Bondarenko, D. (2026). Measurement-based quantum machine learning. Physical Review A, 113(4), 042421. https://doi.org/10.1103/2snk-m8c6

Mantilla Calderón, Luis, et al. "Measurement-based quantum machine learning." Physical Review A, vol. 113, no. 4, 2026, p. 042421. https://doi.org/10.1103/2snk-m8c6