Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

TL;DR

This paper reviews the development of physical reservoir computing, especially quantum neuromorphic processors, as power-efficient solutions for onboard AI in autonomous vehicles, highlighting their potential to reduce power consumption significantly.

Contribution

It provides a perspective on the future of physical reservoir computing and surveys over 200 interdisciplinary studies, emphasizing quantum neuromorphic processors for onboard AI.

Findings

Quantum neuromorphic processors consume less than 1% of onboard power.

Physical reservoir computing offers a promising low-power alternative for onboard AI.

Over 200 research works surveyed in the field.

Abstract

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle's range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.