pennylane-qulacs 0.11.0

PennyLane plugin for Qulacs.

The PennyLane-Qulacs plugin integrates the Qulacs quantum computing framework with PennyLane’s quantum machine learning capabilities.

PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

Qulacs is a software library for quantum computing, written in C++ and with GPU support.

The plugin documentation can be found here: https://pennylane-qulacs.readthedocs.io/en/latest/.

Features

• Provides access to Qulacs’ simulator backend via the qulacs.simulator device

• Support for all PennyLane core functionality

Benchmarks

We ran a 100 executions of 4 layer quantum neural network strongly entangling layer and compared the runtimes between CPU and GPU.

Installation

This plugin requires Python version 3.6 or above, as well as PennyLane and Qulacs. Installation of this plugin, as well as all dependencies, can be done using pip:

$ pip install pennylane-qulacs

Alternatively, you can install PennyLane-Qulacs from the source code by navigating to the top directory and running:

$ python setup.py install

Dependencies

PennyLane-Qulacs requires the following libraries be installed:

• Python >= 3.6

as well as the following Python packages:

• PennyLane >= 0.9

• Qulacs >= 0.1.9

If you currently do not have Python 3 installed, we recommend Anaconda for Python 3, a distributed version of Python packaged for scientific computation.

Tests

To test that the PennyLane-Qulacs plugin is working correctly you can run

$ make test

in the source folder.

Documentation

To build the HTML documentation, go to the top-level directory and run:

$ make docs

The documentation can then be found in the doc/_build/html/ directory.

Contributing

We welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributers to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane.

Authors

PennyLane-Qulacs is the work of many contributors.

If you are doing research using PennyLane and PennyLane-Qulacs, please cite our paper:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, and Nathan Killoran. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

Support

• Source Code: https://github.com/PennyLaneAI/pennylane-qulacs

• Issue Tracker: https://github.com/PennyLaneAI/pennylane-qulacs/issues

• PennyLane Forum: https://discuss.pennylane.ai

If you are having issues, please let us know by posting the issue on our Github issue tracker, or by asking a question in the forum.

License

The PennyLane-Qulacs plugin is free and open source, released under the Apache License, Version 2.0.