qNetVO: The Quantum Network Variational Optimizer#
Simulate and optimize quantum communication networks using quantum computers.
Features#
QNetVO simulates quantum communication networks on differentiable quantum cicuits. The cicuit parameters are optimized with respect to a cost function using automatic differentiation and gradient descent. QNetVO is powered by PennyLane, an open-source framework for cross-platform quantum machine learning.
Simulating Quantum Communication Networks:#
Construct complex quantum network ansatzes from generic quantum circuit compenents.
Simulate the quantum network on a quantum computer or classical simulator.
Optimizing Quantum Communication Networks:#
Use our library of network-oriented cost functions or create your own.
Gradient descent methods for tuning quantum network ansatz settings to minimize the cost.
Quick Start#
Install qNetVO:
$ pip install qnetvo
Install PennyLane:
$ pip install pennylane==0.33
Import packages:
import pennylane as qml
import qnetvo as qnet
Note
For optimal use, qNetVO should be used with PennyLane. QNetVO is currently compatible with PennyLane v0.33.
Contributing#
We welcome outside contributions to qNetVO. Please see the Contributing page for details and a development guide.
How to Cite#
See CITATION.bib for a BibTex reference to qNetVO.
License#
QNetVO is free and open-source. The software is released under the Apache License, Version 2.0. See LICENSE for details and NOTICE for copyright information.
Acknowledgments#
We thank Xanadu, the UIUC Physics Department, and the Quantum Information Science and Engineering Network (QISE-Net) for their support of qNetVO. Work funded by NSF award DMR-1747426.