Quantum implementation of an artificial feed-forward neural network

TL;DR

This paper demonstrates an experimental implementation of a quantum feed-forward neural network on a superconducting quantum processor, showcasing potential advantages in storage and elementary classification tasks, paving the way for hybrid and fully quantum AI solutions.

Contribution

It presents the first experimental realization of a quantum neural network on a superconducting quantum processor with up to 7 qubits, enabling elementary classification tasks beyond classical capabilities.

Findings

Quantum neural network implemented on superconducting hardware

Potential advantage in storage capacity of quantum neurons

Feasible to operate via classical or coherent control

Abstract

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. The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity with respect to their classical counterpart, and it is able to carry out an elementary classification task which would be impossible to achieve with a single node. We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion, thus opening the way to hybrid as well as fully quantum solutions for artificial intelligence to be run on near-term intermediate-scale quantum hardware.