// PennyLane # Software library for programming quantum computers Watch Video ###### Navigation - PennyLane - Key Features - Getting Started - Demos - Documentation ### PennyLane [PennyLane](https://pennylane.ai/) 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. * * * ### Key Features - **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. ### Getting Started Install [PennyLane](https://pennylane.ai/install/) on your computer locally. For an introduction to quantum machine learning, guides and resources are available on PennyLane's quantum machine learning hub: - [What is quantum machine learning?](https://pennylane.ai/whatisqml/) - [Applications, tutorials, and demonstrations](https://pennylane.ai/demonstrations/) - [Frequently asked questions](https://pennylane.ai/faq/) You can also check out our documentation for [quickstart guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane, and detailed [developer guides](https://pennylane.readthedocs.io/en/stable/development/guide.html). ### [Demos](https://pennylane.ai/demonstrations/) 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. \\ \\ Basic tutorial: qubit rotation\\ \\ easy](https://pennylane.ai/demos/tutorial_qubit_rotation/) \\ \\ Variational Quantum Eigensolver (VQE)\\ \\ easy](https://pennylane.ai/demos/tutorial_vqe/) \\ \\ Quantum Approximate Optimization Algorithm\\ \\ easy](https://pennylane.ai/demos/tutorial_qaoa_intro/) \\ \\ Variational classifier\\ \\ easy](https://pennylane.ai/demos/tutorial_variational_classifier/) \\ \\ Quantum Natural Gradient\\ \\ medium](https://pennylane.ai/demos/tutorial_quantum_natural_gradient/) \\ \\ Optimization Of Molecular Geometries\\ \\ medium](https://pennylane.ai/demos/tutorial_mol_geo_opt/) \\ \\ Givens Rotations For Quantum Chemistry\\ \\ easy](https://pennylane.ai/demos/tutorial_givens_rotations/) \\ \\ Quantum Advantage With Gaussian Boson\\ \\ medium](https://pennylane.ai/demos/gbs/) \\ \\ Quantum Transfer Learning\\ \\ hard](https://pennylane.ai/demos/tutorial_quantum_transfer_learning/) \\ \\ Barren Plateaus\\ \\ easy](https://pennylane.ai/demos/tutorial_barren_plateaus/) ### Documentation Access the [full documentation](https://pennylane.readthedocs.io/), or read more in our PennyLane [paper](https://arxiv.org/abs/1811.04968) and differentiable quantum chemistry [paper](https://arxiv.org/abs/2111.09967).