2020/01/31 by Jiayin Chen, Hendra I. Nurdin, Naoki Yamamoto · 105 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Dissipative system #Exploit #IBM #Mechanical and Optical Resonators #Neural Networks and Reservoir Computing #Quantum #Quantum Computing Algorithms and Architecture #Quantum computer #Quantum information #Reservoir computing #Scheme (mathematics) #cs.SY #eess.SY #quant-ph #stat.ML
paper · pdf · doi:10.1103/physrevapplied.14.024065
published in Physical Review Applied 14(2) (American Physical Society) · 9 pages main text, 14 pages appendices, 13 figures. Added implementation scheme using QND measurements and proposal of more efficient implementation schemes without and with QND measurements. To appear in Physical Review Applied
openalex created_date 2020/01/30 · arxiv created 2020/07/23 · openalex publication_date 2020/08/24 · arxiv updated 2020/08/26 · openalex updated_date 2026/08/05
Reservoir computing is a machine learning paradigm that exploits nonlinear dissipative dynamical systems for temporal information processing, and can be combined with quantum computing to form quantum reservoir computers. This study proposes a class of quantum reservoir computers that can be implemented on noisy intermediate-scale quantum (NISQ) computers and possesses the properties required to be reservoir computers, especially universality. Efficient implementation and proof-of-principle demonstration on several cloud-based IBM superconducting quantum devices suggest that the proposed scheme could lead to promising applications of NISQ computers.