Quantum computing models for artificial neural networks

被引：68

作者：

Mangini, S. ^{[1
,2
]}

Tacchino, F. ^{[3
]}

Gerace, D. ^{[1
]}

Bajoni, D. ^{[4
]}

Macchiavello, C. ^{[1
,2
,5
]}

机构：

[1] Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy

[2] Ist Nazl Fis Nucl, Sez Pavia, Via Bassi 6, I-27100 Pavia, Italy

[3] IBM Res Zurich, IBM Quantum, Sumerstr 4, CH-8803 Ruschlikon, Switzerland

[4] Univ Pavia, Dipartimento Ingn Ind & Informaz, Via Ferrata 1, I-27100 Pavia, Italy

[5] CNR INO, Largo E Fermi 6, I-50125 Florence, Italy

来源：

EPL | 2021年 / 134卷 / 01期

基金：

欧盟地平线“2020”;

关键词：

LEARNING ALGORITHM;

D O I：

10.1209/0295-5075/134/10002

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale quantum computing devices have become available in recent years, paving the way for the development of a new paradigm in information processing. Here we give an overview of the most recent proposals aimed at bringing together these ongoing revolutions, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures. We highlight the exciting perspectives in this context, and discuss the potential role of near-term quantum hardware in the quest for quantum machine learning advantage. Copyright (C) 2021 EPLA

引用

页数：7

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