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All Projects → hsim13372 → QCompress-1

hsim13372 / QCompress-1

Licence: MIT license
Quantum Autoencoder Implementation using Forest and OpenFermion

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quantum-computingquantum-algorithmsquantum-machine-learning

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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

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!

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].