Quantum computing models for artificial neural networks

Year: 2021

Authors: Mangini S.; Tacchino F.; Gerace D.; Bajoni D.; Macchiavello C.

Autors Affiliation: Dipartimento di Fisica, Universita di Pavia, Via Bassi 6, Pavia, I-27100, Italy; INFN Sezione di Pavia, Via Bassi 6, Pavia, I-27100, Italy; IBM Quantum, IBM Research-Zurich – S

Abstract: 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.

Journal/Review: EPL

Volume: 134 (1)      Pages from: 10002-1  to: 10002-7

More Information: This research was partly supported by the Italian Ministry of Education, University and Research (MIUR) through the “Dipartimenti di Eccellenza Program (20182022)”, Department of Physics, University of Pavia, the PRIN-2017 project 2017P9FJBS “INPhoPOL”, and by the EU H2020 QuantERA ERA-NET Cofund in Quantum Technologies project QuICHE.
KeyWords: learning algorithm
DOI: 10.1209/0295-5075/134/10002