Metadata-Version: 2.1
Name: qLEET
Version: 0.1.0.dev0
Summary: qLEET is an open-source library for exploring Loss landscape, Expressibility, Entangling                    capability and Training trajectories of noisy parameterized quantum circuits.
Home-page: https://github.com/QLemma/qLEET
Maintainer: QLemma
Maintainer-email: utkarshazad98@gmail.com
License: Apache License 2.0
Description: <p align="left">
          <!-- Continious Integration -->
          <a href="https://github.com/QLemma/qleet/actions?query=workflow%3A%22Continuous+Integration%22"><img alt="GitHub Actions status" src="https://img.shields.io/github/workflow/status/QLemma/qleet/Continuous%20Integration/master?logo=github&style=flat-square"></a>
          <!-- Read the Docs status -->
          <a href="https://qleet.readthedocs.io/en/latest/"><img alt="Read the Docs status" src="https://img.shields.io/readthedocs/qleet"></a>
          <!-- UnitaryFund Support -->
          <a href="http://unitary.fund"><img alt="Unitary Fund" src="https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge" width=175></a>
          <!-- PyPI -->
          <!--   <a href="https://pypi.org/project/qleet">
            <img src="https://img.shields.io/pypi/v/qleet.svg?style=flat-square" />
          </a> -->
          <!-- License -->
          <a href="https://www.apache.org/licenses/LICENSE-2.0">
            <img src="https://img.shields.io/github/license/QLemma/qleet" />
          </a>
        </p>
        
        # qLEET
        
        qLEET is an open-source library for exploring ***Loss landscape***, ***Expressibility***, ***Entangling capability*** and ***Training trajectories*** of noisy parameterized quantum circuits. 
        
        This project is supported by [Unitary Fund](https://unitary.fund/). 
        
        ## Features
        
        1. Will support [Qiskit’s](https://qiskit.org/), [Cirq’s](https://quantumai.google/cirq) and [pyQuil's](https://github.com/rigetti/pyquil) *quantum circuits* and *noise models*.
        2. Provides opportunities to improve existing algorithms like *[VQE](https://www.nature.com/articles/ncomms5213)*, *[QAOA](https://arxiv.org/abs/1411.4028)* by utilizing intuitive insights from the ansatz capability and structure of loss landscape.
        3. Facilitate research in designing new hybrid quantum-classical algorithms.
           
           
        
        ## Examples
        
        ### Properties of an Ansatz
        
        #### Ansatz
        
        <img src="images/ansatz.png" alt="ansatz" width=280 />
        
        #### Expressibility
        
        <img src="images/expressibility.gif" alt="Expressibility" width=480 />
        
        ### Solving MAX-CUT using QAOA 
        
        #### Graph
        
        ![graph](images/graph.png)
        
        #### Loss Landscape
        
        <img src="images/losslandscape.gif" alt="losslandscape" width=480 />
        <!-- ![losslandscape](images/losslandscape.gif) -->
        
        #### Training Path
        
        <img src="images/trainingpath.gif" alt="trainingpath" width=480 />
        <!-- ![trainingpath](images/trainingpath.gif) -->
        
        
        
        ## Contributions
        
        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
        
        ## References
        
        1. [Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms](https://onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900070), Sim, S., Johnson, P. D., & Aspuru‐Guzik, A. Advanced Quantum Technologies, 2(12), 1900070. Wiley. (2019)
        2. [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913), Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein, NIPS 2018, arXiv:1712.09913 [cs.LG] (2018)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Physics
Provides: qleet
Description-Content-Type: text/markdown
