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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:npj quantum information Jg. 5; H. 1
Hauptverfasser: Tacchino, Francesco, Macchiavello, Chiara, Gerace, Dario, Bajoni, Daniele
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 29.03.2019
Nature Publishing Group
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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