(Theory of Quantum Information Toolkit)
The toqito
package is an open source Python library for studying various
objects in quantum information, namely, states, channels, and measurements.
Specifically, toqito
focuses on providing numerical tools to study problems
pertaining to entanglement theory, nonlocal games, matrix analysis, and other
aspects of quantum information that are often associated with computer science.
toqito
aims to fill the needs of quantum information researchers who want
numerical and computational tools for manipulating quantum states,
measurements, and channels. It can also be used as a tool to enhance the
experience of students and instructors in classes pertaining to quantum
information.
Installing
The preferred way to install the toqito
package is via pip
:
pip install toqito
Alternatively, to install, you may also run the following command from the top-level package directory.
python setup.py install
Using
Full documentation along with specific examples and tutorials are provided here: https://toqito.readthedocs.io/.
More information can also be found on the following toqito homepage.
Testing
The pytest
module is used for testing. To run the suite of tests for toqito
,
run the following command in the root directory of this project.
pytest --cov-report term-missing --cov=toqito tests/
Citing
You can cite toqito
using the following DOI:
10.5281/zenodo.4743211
If you are using the toqito
software package in research work, please include
an explicit mention of toqito
in your publication. Something along the lines
of:
To solve problem "X" we used `toqito`; a package for studying certain
aspects of quantum information.
A BibTeX entry that you can use to cite toqito
is provided here:
@misc{toqito,
author = {Vincent Russo},
title = {toqito: A {P}ython toolkit for quantum information, version 1.0.0},
howpublished = {\url{https://github.com/vprusso/toqito}},
month = May,
year = 2021,
doi = {10.5281/zenodo.4743211}
}
The toqito
project has been used in the following works:
-
Philip, Aby, Soorya Rethinasamy, Vincent Russo, and Mark M. Wilde. "Quantum Steering Algorithm for Estimating Fidelity of Separability." arXiv preprint arXiv:2303.07911 (2023).
-
Miszczak, JarosΕaw Adam. "Symbolic quantum programming for supporting applications of quantum computing technologies." arXiv preprint arXiv:2302.09401 (2023).
-
CasalΓ©, Balthazar, Giuseppe Di Molfetta, Sandrine Anthoine, and Hachem Kadri. "Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens." arXiv preprint arXiv:2306.09444 (2023).
Contributing
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
A detailed overview on how to contribute can be found in the contributing guide.