Welcome to the Docs for QCompress!

QCompress is a Python framework for the quantum autoencoder (QAE) algorithm. Using the code, the user can execute instances of the algorithm on either a quantum simulator or a quantum processor provided by Rigetti Computing’s Quantum Cloud Services. For a more in-depth description of QCompress (including the naming convention for the types of qubits involved in the QAE circuit), please go to section Introduction to QAE and QCompress.

For more information about the algorithm, see Romero et al. Note that we deviate from the training technique used in the original paper and instead introduce two alternative autoencoder training schemes that require lower-depth circuits (see Sim et al).


This code is based on an older version written during Rigetti Computing’s hackathon in April 2018. Since then, we’ve updated and enhanced the code, supporting the following features:

  • Executability on Rigetti’s quantum processor(s)
  • Several training schemes for the autoencoder
  • Use of the RESET operation for the encoding qubits (lowers qubit requirement)
  • User-definable training circuit and/or classical optimization routine

API Reference

Indices and tables