PennyLane
Install
Install
Back to Devices

qulacs.simulator

  • Small-Moderate Workloads
  • Large Workloads
  • GPU (simulator)
  • CPU (simulator)
  • Linux
  • macOS
  • Windows

The qulacs.simulator device in the PennyLane-Qulacs plugin provides access to performant state vector simulations on Qulacs backends.

Recommended for:

  • CPU and GPU support.
  • Fast qubit simulations with a parallelized C/C++ backend.
  • Workloads involving more than 20 qubits.
  • All operating systems.

Documentation

To learn more, please visit the device documentation:

  • qulacs.simulator documentation
  • See an overview of the PennyLane-Qulacs plugin.

Installation

The qulacs.simulator device can be installed with:

pip install pennylane-qulacs["cpu"]

Note that you need to include whether to install the CPU version (pennylane-qulacs["cpu"]) or the GPU version (pennylane-qulacs["gpu"]) of Qulacs for it to be installed correctly. Otherwise Qulacs will need to be installed independently:

pip install pennylane-qulacs

For more details on installation and dependencies, visit the PennyLane-Qulacs installation page.

Device Initialization

Initialize the device in PennyLane with:

import pennylane as qp
dev = qp.device('qulacs.simulator', wires=2)

For more details on device settings and keyword arguments, see the device documentation.


Related Content

Demo

Intro to QAOA

Never miss a milestone

Get the latest quantum updates delivered to your inbox.

Join the list
PennyLane

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

Created with ❤️ by Xanadu.

Research

  • Research

  • Performance

  • Hardware and simulators

  • Demos library

  • Compilation hub

  • Quantum datasets

Education

  • Teach

  • Learn

  • Codebook

  • Coding challenges

  • Videos

  • Glossary

Software

  • Install

  • Features

  • PennyLane documentation

  • Catalyst documentation

  • Development guide

  • How-to guides

  • API

  • GitHub


Xanadu

© Copyright 2026 | Xanadu | All rights reserved

TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Privacy policyTerms of serviceCookies policyCode of conduct