Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloAn artificial neuron implemented on an actual quantum processor
Anno di pubblicazione2019
Autore/iTacchino, Francesco; Macchiavello, Chiara; Gerace, Dario; Bajoni, Daniele
Affiliazioni autoriDipartimento di Fisica, Università di Pavia, via Bassi 6, Pavia, I-27100, Dipartimento di Fisica, Università di Pavia, via Bassi 6, I-27100, Pavia, Italy INFN Sezione di Pavia, via Bassi 6, Pavia, I-27100, INFN Sezione di Pavia, via Bassi 6, I-27100, Pavia, Italy; CNR-INO, largo E. Fermi 6, Firenze, I-50125, Italy; Dipartimento di Ingegneria Industriale e dell'Informazione, Università di Pavia, via Ferrata 1, Pavia, I-27100, Italy
Autori CNR e affiliazioni
  • inglese
AbstractArtificial 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.
Lingua abstractinglese
Altro abstract-
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Pagine da26
Pagine a26
Pagine totali8
RivistaNPJ Quantum Information
ISSN: 2056-6387
Titolo chiave: NPJ Quantum Information
Numero volume della rivista5
Fascicolo della rivista1
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85069790474)
  • ISI Web of Science (WoS) (Codice:000466034400001)
Parole chiaveartificial intelligence; neural networks
Link (URL, URI)
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Scadenza embargo-
Data di accettazione01/03/2019
Note/Altre informazioniWe 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.
Strutture CNR
  • INO — Istituto nazionale di ottica
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-