Scientific Results

Quantum implementation of an artificial feed-forward neural network

Year: 2020

Authors: Tacchino F., Barkoutsos P., Macchiavello C., Tavernelli I., Gerace D., Bajoni D.

Autors Affiliation: Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy; IBM Res Zurich, IBM Quantum, Saumerstr 4, CH-8803 Ruschlikon, Switzerland; INFN, Sez Pavia, Via Bassi 6, I-27100 Pavia, Italy; CNR, INO, Largo E Fermi 6, I-50125 Florence, Italy; Univ Pavia, Dipartimento Ingn Ind & Informaz, Via Ferrata 1, I-27100 Pavia, Italy

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.

Journal/Review: QUANTUM SCIENCE AND TECHNOLOGY

Volume: 5 (4)      Pages from: 044010  to: 044010

KeyWords: quantum neural networks; quantum algorithms; near-term quantum processors
DOI: 10.1088/2058-9565/abb8e4

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