Volume 134, Number 1, April 2021
|Number of page(s)||7|
|Published online||14 May 2021|
Quantum computing models for artificial neural networks
1 Dipartimento di Fisica, Università di Pavia - Via Bassi 6, I-27100, Pavia, Italy
2 INFN Sezione di Pavia - Via Bassi 6, I-27100, Pavia, Italy
3 IBM Quantum, IBM Research-Zurich - Sümerstrasse 4, CH-8803 Rüschlikon, Switzerland
4 Dipartimento di Ingegneria Industriale e dell'Informazione, Università di Pavia - Via Ferrata 1, I-27100, Pavia, Italy
5 CNR-INO - Largo E. Fermi 6, I-50125, Firenze, Italy
(a) email@example.com (corresponding author)
Received: 5 February 2021
Accepted: 18 March 2021
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.
© 2021 EPLA
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