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.
Currently, PennyLane-Qulacs provides one Qulacs device for PennyLane:
We ran a 100 executions of 4 layer quantum neural network strongly entangling layer and compared the runtimes between CPU and GPU.
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
dev = qml.device('qulacs.simulator', wires=XXX)