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Machine learning on quantum hardware. Connect to quantum hardware using PyTorch, TensorFlow, JAX, Keras, or NumPy. Build rich and flexible hybrid quantum-classical models.
Just in time compilation. Experimental support for just-in-time compilation. Compile your entire hybrid workflow, with support for advanced features such as adaptive circuits, real-time measurement feedback, and unbounded loops. See Catalyst for more details.
Device-independent. Run the same quantum circuit on different quantum backends. Install plugins to access even more devices, including Strawberry Fields, Amazon Braket, IBM Q, Google Cirq, Rigetti Forest, Qulacs, Pasqal, Honeywell, and more.
Batteries included. Built-in tools for quantum machine learning, optimization, and quantum chemistry. Rapidly prototype using built-in quantum simulators with backpropagation support.
PennyLane requires Python version 3.9 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:
