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lightning.gpu

  • Built by PennyLane
  • QJIT
  • Large Workloads
  • Distributed Simulation
  • Performance
  • GPU (simulator)
  • Linux

lightning.gpu is PennyLane’s GPU-extension of lightning.qubit; it’s our most performant all-around device for heavy workloads, made possible by NVIDIA’s cuQuantum SDK.

Recommended for:

  • Large state vector simulations with 20+ qubits.
  • Workflows that may benefit from parallelization over multiple GPUs.
  • Fast gradients with parallel adjoint differentiation.
  • Linux operating systems.

Documentation

To learn more, please visit the device documentation:

  • lightning.gpu documentation

See all Lightning devices:

  • lightning.gpu
  • lightning.kokkos
  • lightning.qubit
  • lightning.tensor

Installation

The lightning.gpu device can be installed with:

pip install pennylane-lightning-gpu

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


Device Initialization

Initialize the device in PennyLane with:

import pennylane as qml dev = qml.device('lightning.gpu', wires=20)

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


Related Content

Demo

How to use Catalyst with Lightning-GPU

Blog

Distributing quantum simulations using lightning.gpu with NVIDIA cuQuantum

Blog

Lightning-fast simulations with PennyLane and the NVIDIA cuQuantum SDK

PennyLane

PennyLane is an open-source software framework for quantum machine learning, quantum chemistry, and quantum computing, with the ability to run on all hardware. Built with ❤️ by Xanadu.

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