A Python framework for the quantum autoencoder algorithm
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), click here.
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
There are a few options for installing QCompress:
To install QCompress using pip, execute:
pip install qcompress
To install QCompress using conda, execute:
conda install -c rigetti -c hsim13372 qcompress
To instead install QCompress from source, clone this repository, cd into it, and run:
git clone https://github.com/hsim13372/QCompress cd QCompress python -m pip install -e .
Try executing import qcompress to test the installation in your terminal.
Note that the pyQuil version used requires Python 3.6 or later. For installation on a user QMI, please click here.
We provide several Jupyter notebooks to demonstrate the utility of QCompress. We recommend going through the notebooks in the order shown in the table (top-down).
Simulates the compression of the ground states of the hydrogen molecule. Uses OpenFermion and grove to generate data. Demonstrates the “halfway” training scheme.
Simulates the compression of a two-qubit data set. Outlines how to run an instance on an actual device. Demonstrates the “full with reset” training scheme.
Shows user how to run landscape scans for small (few-parameter) instances. Demonstrates setup of the “full with no reset” training scheme.
We note that there is a lot of room for improvement and fixes. Please feel free to submit issues and/or pull requests!
How to cite
When using QCompress for research projects, please cite:
Sukin Sim, Yudong Cao, Jonathan Romero, Peter D. Johnson and Alán Aspuru-Guzik. A framework for algorithm deployment on cloud-based quantum computers. arXiv:1810.10576. 2018.
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