========= QCompress

.. image:: https://readthedocs.org/projects/qcompress/badge/?version=latest :target: https://qcompress.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

Description

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 <https://www.rigetti.com/qcs>. For a more in-depth description of QCompress (including the naming convention for the types of qubits involved in the QAE circuit), click here <https://github.com/hsim13372/QCompress/blob/master/examples/intro.rst>.

For more information about the algorithm, see Romero et al <https://arxiv.org/abs/1612.02806>. 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 <https://arxiv.org/abs/1810.10576>).

Features

This code is based on an older version <https://github.com/hsim13372/QCompress-1>__ 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

Installation

There are a few options for installing QCompress:

1. To install QCompress using pip, execute:

.. code-block:: bash

pip install qcompress

2. To install QCompress using conda, execute:

.. code-block:: bash

conda install -c rigetti -c hsim13372 qcompress

3. To instead install QCompress from source, clone this repository, cd into it, and run:

.. code-block:: bash

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 <https://github.com/hsim13372/QCompress/blob/master/qmi_instructions.rst>__.

Examples

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).

.. csv-table:: :header: Notebook, Feature(s)

qae_h2_demo.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/qae_h2_demo.ipynb>, Simulates the compression of the ground states of the hydrogen molecule. Uses OpenFermion and grove to generate data. Demonstrates the "halfway" training scheme. qae_two_qubit_demo.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/qae_two_qubit_demo.ipynb>, 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. run_landscape_scan.ipynb <https://github.com/hsim13372/QCompress/blob/master/examples/run_landscape_scan.ipynb>__, Shows user how to run landscape scans for small (few-parameter) instances. Demonstrates setup of the "full with no reset" training scheme.

Disclaimer

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 <https://arxiv.org/abs/1810.10576>`__. 2018.

Authors

Sukin (Hannah) Sim <https://github.com/hsim13372>__ (Harvard), Zapata Computing, Inc. <https://zapatacomputing.com/>__