// Lightning

High-performance quantum simulators



Lightning is a high-performance quantum simulator, designed to provide the ultimate performance for quantum machine learning applications. Written in C++ and accessible via Python, there are two simulators in the Lightning family: lightning.qubit and lightning.gpu, ensuring you can simulate large workflows quickly, whether you are using GPUs locally or running on supercomputers. In addition, Lightning integrates seamlessly with PennyLane, providing cutting-edge techniques for quantum optimization for the truly fastest quantum machine learning workflows.

Key Features

  • Run anywhere.
    With support for x86, ARM, AARCH64, and PowerPC, Lightning supports the widest variety of architectures, ensuring that you can use it with ease anywhere, from laptops to the cloud to supercomputing centers.
  • GPU support powered by NVIDIA.
    Take advantage of GPU clusters with lightning.gpu powered by NVIDIA's CuQuantum SDK for GPU accelerated circuit simulation.
  • Optimized for machine learning.
    Lightning was built with machine learning in mind, so comes with built-in support for highly efficient computations of quantum gradients. Train and optimize your quantum algorithms with scale.
  • Fast.
    Built-in C++ kernels for a large variety of different quantum gates and operations ensures that quantum circuits are optimized for performance no matter your application, from quantum chemistry to machine learning.
  • Seamless PennyLane integration.
    Use Lightning in combination with PennyLane for direct access to PennyLane optimizers and algorithms. In addition, connect your large scale simulations with machine learning frameworks such as PyTorch, TensorFlow, and JAX.

Getting Started

lightning.qubit is installed automatically alongside PennyLane.

Option 1

Access Lightning with PennyLane through Xanadu Cloud

Option 2

Install Lightning and PennyLane on your computer locally


Once you have Lightning up and running, explore our tutorials and demonstrations.


Access the full documentation for more information.