PennyLane comes with built-in support for several quantum devices:
A simple state-vector qubit simulator written in Python, with Autograd, JAX, TensorFlow, and Torch backends.
A good choice for optimizations with a moderate number of qubits and parameters with exact expectation values.
A mixed-state qubit simulator written in Python, with Autograd, JAX, TensorFlow, and Torch backends.
A good choice for simulating noisy circuits and quantum channels.
A simple quantum photonic simulator written in Python.
A good choice for optimizing photonic systems.
External quantum devices can be easily added to PennyLane by installing plugins. These plugins are installed separately, providing a rich ecosystem integrating popular quantum software development libraries with the hybrid optimization capabilities of PennyLane.
Below are a list of official PennyLane plugins supported by the PennyLane development team.
In addition to the official plugins, there are also plugins created by the PennyLane community.
To write your own PennyLane-compatible plugin, the best place to start is our overview of the plugin API. Have a plugin you would like to have listed here? Let us know at software@xanadu.ai.