Quantum computing models for artificial neural networks

TL;DR

This paper reviews recent approaches to implementing artificial neural network functionalities on quantum computing architectures, exploring potential advantages and the role of near-term quantum hardware in quantum machine learning.

Contribution

It provides an overview of the latest proposals integrating neural networks with quantum computing and discusses future perspectives and hardware implications.

Findings

Quantum architectures can implement neural network functionalities.

Potential for quantum advantage in machine learning tasks.

Near-term quantum hardware may enable practical quantum neural networks.

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.