Welcome to qLEET’s documentation!

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

Will support Qiskit’s, Cirq’s and pyQuil’s quantum circuits and noise models.

Our package provides opportunities to improve existing algorithms like VQE, QAOA by utilizing intuitive insights from the ansatz capability and structure of loss landscape.

The aim of the library is to facilitate research in designing new hybrid quantum-classical algorithms.

Types of Plots

There are several types of analysis supported by our module. They are:

• Expressibility Plot.

• Loss Landscape Plot.

• Training Path Plot.

• Entanglement Alibility Value.

• Histogram Plot.

More types of analysis will be added, be sure to raise an issue for feature requests on the repository.

Examples of Circuit

The library comes with a few Pre-built circuits to which you can analyze out of the box.

One of those is QAOA for computing Max Cut of a Graph.

Contents:

• qleet package
  • Subpackages
    • qleet.analyzers package
      • Submodules
      • qleet.analyzers.entanglement module
      • qleet.analyzers.expressibility module
      • qleet.analyzers.loss_landscape module
      • qleet.analyzers.training_path module
      • qleet.analyzers.histogram module
      • Module contents
    • qleet.examples package
      • Submodules
      • qleet.examples.qaoa_maxcut module
      • Module contents
    • qleet.interface package
      • Submodules
      • qleet.interface.circuit module
      • qleet.interface.dashboard module
      • qleet.interface.metas module
      • qleet.interface.metric_spec module
      • Module contents
    • qleet.simulators package
      • Submodules
      • qleet.simulators.circuit_simulators module
      • qleet.simulators.pqc_trainer module
      • Module contents
  • Module contents

Indices and tables

• Index

• Module Index

• Search Page