qLEET is an open-source library for exploring Loss landscape, Expressibility, Entangling capability and Training trajectories of noisy parameterized quantum circuits.

Key Features

1. Will support Qiskit’s, Cirq’s and pyQuil's quantum circuits and noise models.
2. Provides opportunities to improve existing algorithms like VQE, QAOA by utilizing intuitive insights from the ansatz capability and structure of loss landscape.
3. Facilitate research in designing new hybrid quantum-classical algorithms.

Installation

qLEET requires Python version 3.7 and above. Installation of qLEET, as well as all its dependencies, can be done using pip:

python -m pip install qleet

Examples

Properties of an Ansatz

Ansatz

Expressibility and Entanglement Spectrum

Solving MAX-CUT using QAOA

Problem Graph

Loss Landscape and Training Trajectories

Contributing to qLEET

We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:

• Reporting a bug
• Submitting a fix
• Proposing new features

Feel free to open an issue on this repository or add a pull request to submit your contribution. Adding test cases for any contributions is a requirement for any pull request to be merged

Financial Support

This project has been supported by Unitary Fund.

License

qLEET is free and open source, released under the Apache License, Version 2.0.

References

1. Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms, Sim, S., Johnson, P. D., & Aspuru‐Guzik, A. Advanced Quantum Technologies, 2(12), 1900070. Wiley. (2019)
2. Visualizing the Loss Landscape of Neural Nets, Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein, NIPS 2018, arXiv:1712.09913 [cs.LG] (2018)