Feedback-Driven Quantum Reservoir Computing for Time-Series Analysis

作     者：Kaito Kobayashi Keisuke Fujii Naoki Yamamoto 

作者机构：Department of Applied Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka 560-0043 Japan RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan 

出 版 物：《PRX Quantum》 (PRX. Quantum.)

年 卷 期：2024年第5卷第4期

页      面：040325-040325页

核心收录：

基　　金：Ministry of Economy, Trade and Industry, METI Information-Technology Promotion Agency, IPA Japan Science and Technology Agency, JST, (JPMJBS2418) Japan Science and Technology Agency, JST Japan Society for the Promotion of Science, JSPS, (JP24KJ0872) Japan Society for the Promotion of Science, JSPS JST COI-NEXT, (JPMJPF2014, JPMXS0118067285) Ministry of Education, Culture, Sports, Science and Technology, MEXT, (JPMXS0120319794) Ministry of Education, Culture, Sports, Science and Technology, MEXT 

主　　题：Artificial intelligence Quantum algorithms Quantum circuits Quantum computation Quantum computing models Quantum simulation 

摘      要：Quantum reservoir computing (QRC) is a highly promising computational paradigm that leverages quantum systems as a computational resource for nonlinear information processing. While its application to time-series analysis is eagerly anticipated, prevailing approaches suffer from the collapse of the quantum state upon measurement, resulting in the erasure of temporal input memories. Neither repeated initializations nor weak measurements offer a fundamental solution, as the former escalates the time complexity while the latter restricts the information extraction from the Hilbert space. To address this issue, we propose the feedback-driven QRC framework. This methodology employs projective measurements on all qubits for unrestricted access to the quantum state, with the measurement outcomes subsequently fed back into the reservoir to restore the memory of prior inputs. We demonstrate that our QRC successfully acquires the fading-memory property through the feedback connections, a critical aspect in time-series processing. Notably, analysis of measurement trajectories reveals three distinct phases depending on the feedback strength, with the memory performance maximized at the edge of chaos. We also evaluate the predictive capabilities of our QRC, demonstrating its suitability for forecasting signals originating from quantum spin systems.