PennyLane-Qulacs Plugin

Release

0.12.0-dev

_images/puzzle_qulacs.png

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.

Once PennyLane-Qulacs is installed, the provided Qulacs devices can be accessed straight away in PennyLane, without the need to import any additional packages.

Devices

Currently, PennyLane-Qulacs provides one Qulacs device for PennyLane:


Benchmarks

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

https://raw.githubusercontent.com/soudy/pennylane-qulacs/master/images/qnn_cpu_vs_gpu.png

https://raw.githubusercontent.com/soudy/pennylane-qulacs/master/images/qulacs_table.png

Tutorials

To see the PennyLane-Qulacs plugin in action, you can use any of the qubit based demos from the PennyLane documentation, for example the tutorial on qubit rotation, and simply replace 'default.qubit' with the 'qulacs.simulator' device:

dev = qml.device('qulacs.simulator', wires=XXX)