Welcome to MentPy’s documentation¶

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 🙂

@software{Mantilla_Mentpy_2023,
   title = {{MentPy: A python package for simulating and training QML models in the MBQC framework.}},
   author = {Mantilla Calderón, Luis},
   year = {2023},
   url = {https://github.com/mentpy/mentpy},
}

L. Mantilla Calderón, MentPy: A python package for simulating and training QML models in the MBQC framework, (2023). https://github.com/mentpy/mentpy

Mantilla Calderón, L. (2023). MentPy: A python package for simulating and training QML models in the MBQC framework. Retrieved from https://github.com/mentpy/mentpy

Mantilla Calderón, Luis. MentPy: A python package for simulating and training QML models in the MBQC framework. 2023. Web. https://github.com/mentpy/mentpy