QCVV and Benchmarking
Forest Benchmarking: QCVV using PyQuil
A library for quantum characterization, verification, validation (QCVV), and benchmarking using pyQuil.
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.
git clone https://github.com/rigetti/forest-benchmarking.git cd forest-benchmarking/ pip install numpy scipy pip install -e .
pip install numpy scipy pip install forest-benchmarking
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
/examples/ directory along with the standard documentation found in
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.
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.
If you use Forest Benchmarking, please cite it via the BibTeX file.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for forest-benchmarking-0.8.0.tar.gz
Hashes for forest_benchmarking-0.8.0-py3-none-any.whl