An artificial neuron implemented on an actual quantum processor

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...

Full description

Saved in:
Bibliographic Details
Published in:npj quantum information Vol. 5; no. 1
Main Authors: Tacchino, Francesco, Macchiavello, Chiara, Gerace, Dario, Bajoni, Daniele
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 29.03.2019
Nature Publishing Group
Subjects:
639/766/259
639/766/483
639/766/483/481
Artificial intelligence
Classical and Quantum Gravitation
Learning algorithms
Neural networks
Physics
Physics and Astronomy
Quantum Computing
Quantum Field Theories
Quantum Information Technology
Quantum Physics
Relativity Theory
Scaling
Spintronics
String Theory
ISSN:2056-6387, 2056-6387
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2056-6387
2056-6387
DOI:10.1038/s41534-019-0140-4