PennyLane-Qulacs Plugin¶
- Release
0.37.0-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/qnn_cpu_vs_gpu.png)
![https://raw.githubusercontent.com/soudy/pennylane-qulacs/master/images/qulacs_table.png](https://raw.githubusercontent.com/soudy/pennylane-qulacs/master/images/qulacs_table.png)
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)