Work

Enhanced predictive capability for chaotic dynamics by modified quantum reservoir computing

Year: 2024

Type: article

Abstract: Deducing the states of spatiotemporally chaotic systems (SCSs) as they evolve in time is crucial for various applications. However, generally achieving this is a dramatic challenge due to the complexity of nonperiodic dynamics and the hardness of obtaining robust solutions. In recent years, there has been a growing interest in approaching the problem using both classical and quantum machine learning methods. Although effective for predicting SCSs within a relatively short time, the current schemes are not capable of providing robust solutions for longer times than the training time. Here we propose an approach for advancing the prediction of chaotic behavior. Our approach can be viewed as a unique quantum reservoir computing scheme, which can simultaneously capture the linear and the nonlinear features of input data and evolve under a modified Hamiltonian. Our work presents an alternative approach to handling SCSs. Published by the American Physical Society 2024

Source: Physical Review Research

DOI: 10.1103/physrevresearch.6.043183

Authors: Longhan Wang, Yifan Sun, Xiangdong Zhang

Institution: Beijing Institute of Technology

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China