Quantum neuromorphic computing

TL;DR

Quantum neuromorphic computing leverages quantum hardware to emulate neural networks, potentially enhancing computational speed by utilizing quantum properties through various digital and analog implementations.

Contribution

This paper reviews emerging quantum neuromorphic computing paradigms, comparing digital and analog approaches, and discusses recent experimental advancements in the field.

Findings

Quantum neuromorphic systems can utilize quantum properties for neural network computation.

Different implementations offer unique advantages in speed and efficiency.

Recent experiments demonstrate practical progress in quantum neuromorphic hardware.

Abstract

Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits, and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and compute. We discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.