hsim13372 / QCompress-1

Quantum Autoencoder Implementation using Forest and OpenFermion

Rigetti Hackathon Project: QCompress

QCompress is an implementation of the quantum autoencoder using Forest and OpenFermion.

Objective

This was our project for Rigetti Computing's first quantum computing hackathon. Our goal was to create a flexible framework for the quantum autoencoder (QAE) that can be used to compress quantum data. This autoencoder implementation is based on the work by Romero et al.

Demo

We've included a demonstration of the quantum autoencoder code in qae_h2_demo.ipynb, in which we compress the ground states of molecular hydrogen.

Dependencies and Versions Used

• Python 3.5
• pyQuil 2.0.0
• OpenFermion 0.6
• forestopenfermion 0.0.3
• Grove 1.6.0

Authors

Sukin Sim (Hannah), Evan Anderson, Eric Brown, Jonathan Romero

Fixes

We note that there is a lot of room for improvement and fixes. Please feel free to submit pull requests!