"PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network."

Oh wow, that's, uh, that's a concept.

"Key Features:"

"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."

"Follow the gradient: Hardware-friendly automatic differentiation of quantum circuits."

"Batteries included; Built-in tools for quantum machine learning, optimization, and quantum chemistry. Rapidly prototype using built-in quantum simulators with backpropagation support."

I don't have a quantum computer, so I'll leave it to all of you to run this and tell me how it goes.

PennyLaneAI / pennylane

#solidstatelife #ai #differentiableprogramming #quantumcomputing