Software library for programming quantum computers
PennyLane is the leading tool for programming quantum computers. A cross-platform Python library, it enables a new paradigm — quantum differentiable programming — that enables seamless integration with machine learning tools. Train a quantum computer like you would train a neural network. PennyLane also supports a comprehensive set of features, simulators, hardware, and community-led resources that enable users of all levels to easily build, optimize and deploy quantum-classical applications.
Write once, run anywhere. Change your quantum device in a single line to swap between simulators and hardware; no other changes to your program needed.
Simulators and hardware, all in one place. Access the fastest all-purpose simulators and the widest hardware availability. Seamlessly combine high-performance compute and GPUs with quantum hardware from Xanadu, Amazon Braket, Google, IBM, Rigetti and more; move from rapid iteration to hardware testing with ease.
A global community. From our curated collection of tutorials, support forum, demonstrations, and videos, the PennyLane community is the place to go to learn quantum computing and quantum machine learning.
Built-in automatic differentiation of quantum circuits. PennyLane knows how to differentiate through all quantum devices, whether simulators or hardware. And it automatically chooses the best algorithms for the job.
Machine learning on quantum hardware. Connect quantum hardware seamlessly to PyTorch, TensorFlow, JAX, and NumPy to build rich and flexible quantum-classical models.
Everything included. PennyLane's core tenet is flexibility. Build the algorithms you envision — we won't get in your way. But when you need those extra tools, they are there, from quantum optimizers to quantum chemistry algorithms.
For an introduction to quantum machine learning, guides and resources are available on PennyLane's quantum machine learning hub:
Get familiar with more advanced applications of PennyLane and quantum machine learning. Learn how to implement a variational quantum eigensolver, play around with quantum chemistry simulations, solve graph problems such as MaxCut, or implement quantum machine learning circuits on real quantum hardware.