An artificial neuron implemented on an actual quantum processor

Year: 2019

Authors: Tacchino F., Macchiavello C., Gerace D., Bajoni D.

Autors Affiliation: Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy; 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 neural networks are the heart of machine learning algorithms and artificial intelligence. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt’s “perceptron”, but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a binary-valued perceptron, which shows exponential advantage in storage resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives answers consistent with the expected results. We show that this quantum model of a perceptron can be trained in a hybrid quantum-classical scheme employing a modified version of the perceptron update rule and used as an elementary nonlinear classifier of simple patterns, as a first step towards practical quantum neural networks efficiently implemented on near-term quantum processing hardware.

Journal/Review: NPJ QUANTUM INFORMATION

Volume: 5 (1)      Pages from: 26  to: 26

More Information: We acknowledge the University of Pavia Blue Sky Research project number BSR1732907. This research was also supported by the Italian Ministry of Education, University and Research (MIUR): “Dipartimenti di Eccellenza Program (2018-2022)”, Department of Physics, University of Pavia. We acknowledge use of the IBM Quantum Experience for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM company or the IBM-Q team.
KeyWords: artificial intelligence; neural networks
DOI: 10.1038/s41534-019-0140-4

Citations: 143
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