|toqito⟩: 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.

|toqito⟩ focuses on providing numerical tools to study problems about 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 about quantum information.

Installing

|toqito⟩ is available via PyPi for Linux, and macOS, with support for Python 3.10 to 3.12.

pip install toqito

Examples

Full documentation, along with specific examples and tutorials, is provided here: https://toqito.readthedocs.io/.

Chat with us in our |toqito⟩ channel on Discord.

Example: Nonlocal games

Nonlocal games are a mathematical framework that abstractly models a physical system. The CHSH game is a subtype of nonlocal game referred to as an XOR game that characterizes the seminal CHSH inequality.

For XOR games, there exist optimization problems (that are provided via |toqito⟩) that one can compute to attain the optimal values of such games when the players use either a classical or quantum strategy.

# Calculate the classical and quantum value of the CHSH game.
import numpy as np
from toqito.nonlocal_games.xor_game import XORGame

# The probability matrix.
prob_mat = np.array([[1/4, 1/4], [1/4, 1/4]])

# The predicate matrix.
pred_mat = np.array([[0, 0], [0, 1]])

# Define CHSH game from matrices.
chsh = XORGame(prob_mat, pred_mat)

chsh.classical_value()
# 0.75
chsh.quantum_value()
# 0.8535533

Indeed, using a quantum strategy for the CHSH game gives the known optimal result of $\frac{1}{4}\left(2 + \sqrt{2}\right) \approx 0.8535...$

Example: Quantum state distinguishability

Quantum state distinguishability is a fundamental task in quantum information theory. Consider the set of four Bell states:

$$ \begin{equation} \begin{aligned} |\psi_0\rangle = \frac{1}{\sqrt{2}} \left(|00\rangle + |11\rangle\right), \quad |\psi_1\rangle = \frac{1}{\sqrt{2}} \left(|00\rangle - |11\rangle\right), \\ |\psi_2\rangle = \frac{1}{\sqrt{2}} \left(|01\rangle + |10\rangle\right), \quad |\psi_3\rangle = \frac{1}{\sqrt{2}} \left(|01\rangle - |10\rangle\right). \end{aligned} \end{equation} $$

The optimal probability of globally distinguishing the four Bell states (assuming an equal weighting of probability) is 1 (i.e., it can be performed perfectly). However, under a more restrictive set of measurements (such as PPT measurement operators), the optimal probability of distinguishing the four Bell states using PPT operators is 1/2.

|toqito⟩ offers a wide suite of functionality for computing the distinguishability of quantum states:

from toqito.states import bell
from toqito.state_opt import state_distinguishability, ppt_distinguishability

# Define the set of states as the four Bell states:
states = [bell(0), bell(1), bell(2), bell(3)]

# Distinguishing four Bell states (global measurements): 0.9999999999767388
pos_res, _ = state_distinguishability(states)
print(f"Distinguishing four Bell states (global measurements): {pos_res}")

# Distinguishing four Bell states (PPT measurements): 0.5000000000098367
ppt_res, _ = ppt_distinguishability(states, subsystems=[0], dimensions=[2, 2])
print(f"Distinguishing four Bell states (PPT measurements): {ppt_res}")

Consult the tutorials for additional examples and information.

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

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},
   howpublished = {\url{https://github.com/vprusso/toqito}},
   month        = May,
   year         = 2021,
   doi          = {10.5281/zenodo.4743211}
 }

Research

The |toqito⟩ project is, first and foremost, a quantum information theory research tool. Consult the following open problems wiki page for a list of certain solved and unsolved problems in quantum information theory in which |toqito⟩ could be potentially helpful in probing. Feel free to add to this list and/or contribute solutions!

The |toqito⟩ project has been used or referenced in the following works:

• Philip, Aby "On Multipartite Entanglement and Its Use", (2024).

• Gupta, Tathagata and Mushid, Shayeef and Russo, Vincent and Bandyopadhyay, Somshubhro "Optimal discrimination of quantum sequences", (2024).

• Bandyopadhyay, Somshubhro and Russo, Vincent "Distinguishing a maximally entangled basis using LOCC and shared entanglement", (2024).

• Tavakoli, Armin and Pozas-Kerstjens, Alejandro and Brown, Peter and Araújo, Mateus "Semidefinite programming relaxations for quantum correlations", (2023).

• Johnston, Nathaniel and Russo, Vincent and Sikora, Jamie "Tight bounds for antidistinguishability and circulant sets of pure quantum states", (2023).

• Pelofske, Elijah and Bartschi, Andreas and Eidenbenz, Stephan and Garcia, Bryan and Kiefer, Boris "Probing Quantum Telecloning on Superconducting Quantum Processors", (2023).

• Philip, Aby and Rethinasamy, Soorya and Russo, Vincent and Wilde, Mark. "Quantum Steering Algorithm for Estimating Fidelity of Separability.", Quantum 8, 1366, (2023).

• Miszczak, Jarosław Adam. "Symbolic quantum programming for supporting applications of quantum computing technologies.", (2023).

• Casalé, Balthazar and Di Molfetta, Giuseppe and Anthoine, Sandrine and Kadri, Hachem. "Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens.", (2023).

• Russo, Vincent and Sikora, Jamie "Inner products of pure states and their antidistinguishability", Physical Review A, Vol. 107, No. 3, (2023).

Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

A detailed overview of how to contribute can be found in the contributing guide.

License

MIT License