Awesome

Forest Benchmarking: QCVV using PyQuil

A library for quantum characterization, verification, validation (QCVV), and benchmarking using pyQuil.

Installation

forest-benchmarking can be installed from source or via the Python package manager PyPI.

Note: NumPy and SciPy must be pre-installed for installation to be successful, due to cvxpy.

Source

git clone https://github.com/rigetti/forest-benchmarking.git
cd forest-benchmarking/
pip install numpy scipy
pip install -e .

PyPI

pip install numpy scipy
pip install forest-benchmarking

Library Philosophy

The core philosophy of forest-benchmarking is to separate:

• Experiment design and or generation
• Data collection
• Data analysis
• Data visualisation

We ask that code contributed to this repository respect this separation. We also ask that an example of how to use your contributed code is placed in the /examples/ directory along with the standard documentation found in /docs/.

Testing

The unit tests can be run locally using pytest, but beware that the test dependencies must be installed beforehand using pip install -r requirements.txt.

Disclaimer

This package is currently in alpha (v0.x), and therefore you should not expect that APIs will necessarily be stable between releases. Code that depends on this package in its current state is very likely to break when the package version changes, so we encourage you to pin the version you use, and update it consciously when necessary.

Citation

If you use Forest Benchmarking, please cite it via the BibTeX file.