Overview

The Quantum Machine Learning Toolbox consists of the two CircuitLearner classes for automatic and numerical differentation. These classes can be used for optimization, supervised and unsupervised learning with variational circuits.

Software components

Frontend:

  • qmlt.helpers - collection of learner-independent helpers; these can be used with either backend.

Numerical backend:

TF backend:

  • qmlt.tf - learner class for the training of user-provided quantum circuits.
  • qmlt.tf.helpers - collection of helpers to set up an experiment with the learners.

Code details

qmlt module

qmlt.version()[source]

Get version number of the QMLT.

Returns:The package version number
Return type:str