Quantum implementation of an artificial feed-forward neural network

Understand

Artificial intelligence algorithms largely build on multi-layered neural networks.

• Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run.
• Quantum computing promises to solve certain tasks much more efficiently than any classical computing machine, and actual quantum processors are now becoming available through cloud access to perform experiments and testing also outside of research labs.
• Here we show in practice an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor using up to 7 active qubits.