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

A. H. Abbas,

Hend Abdel-Ghani,

Ivan S. Maksymov

и другие.

Dynamics, Год журнала: 2024, Номер 4(3), С. 643 - 670

Опубликована: Авг. 12, 2024

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

Язык: Английский

Reservoir Computing Using Measurement-Controlled Quantum Dynamics DOI Open Access

A. H. Abbas,

Ivan S. Maksymov

Electronics, Год журнала: 2024, Номер 13(6), С. 1164 - 1164

Опубликована: Март 21, 2024

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce quantum RC probed atom in cavity. The experiences coherent driving at particular rate, leading measurement-controlled evolution. proposed can make fast reliable forecasts using small number artificial neurons compared with traditional algorithm. We theoretically validate operation reservoir, demonstrating its potential be used error-tolerant applications, where approximate approaches may feasible conditions limited computational energy resources.

Язык: Английский

Процитировано

5
Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective DOI Creative Commons

A. H. Abbas,

Hend Abdel-Ghani,

Ivan S. Maksymov

и другие.

Dynamics, Год журнала: 2024, Номер 4(3), С. 643 - 670

Опубликована: Авг. 12, 2024

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

Язык: Английский

Процитировано

3