PennyLane-Qulacs Plugin

Release

0.17.0-dev

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

Check out these demos 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)