Temporal Information Processing on Noisy Quantum Computers
Jiayin Chen, Hendra I. Nurdin, Naoki Yamamoto
Abstract
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.
Topics & Concepts
Computer scienceReservoir computingQuantum computerExploitIBMQuantumQuantum informationTheoretical computer scienceScheme (mathematics)Dissipative systemNonlinear systemClass (philosophy)Computer engineeringInformation processingQuantum algorithmComputational scienceComputationNoise (video)AlgorithmMicrocomputerQuantum stateNeural Networks and Reservoir ComputingQuantum Computing Algorithms and ArchitectureMechanical and Optical Resonators