Natural quantum reservoir computing for temporal information processing

Abstract

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

References in corpus (6)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Physical reservoir computing -- An introductory perspective
• Modeling and Control of Quantum Systems: An Introduction
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Reconstructing quantum states with quantum reservoir networks
• Learning Temporal Quantum Tomography

Cited by in corpus (34)

• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Hybrid quantum-classical reservoir computing of thermal convection flow
• Reservoir Computing and Photoelectrochemical Sensors: A Marriage of Convenience
• Dissipation as a resource for Quantum Reservoir Computing
• Feedback-driven quantum reservoir computing for time-series analysis
• On fundamental aspects of quantum extreme learning machines
• Quantum reservoir computing in finite dimensions
• Exploring quantum mechanical advantage for reservoir computing
• Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data
• Microwave signal processing using an analog quantum reservoir computer
• How quantum computing can enhance biomarker discovery
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
• Extending echo state property for quantum reservoir computing
• Improving the Performance of Echo State Networks Through State Feedback
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics
• Simple Hamiltonian dynamics is a powerful quantum processing resource
• Demonstration of system-bath physics on a gate-based quantum computer
• Effects of dissipation in reservoir computing using a spin qubit array
• An introduction to reservoir computing
• High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins
• Generating Quantum Reservoir State Representations with Random Matrices
• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• Optimal quantum reservoir learning in proximity to universality
• Shannon Entropy Helps Optimize the Performance of a Frequency-Multiplexed Extreme Learning Machine
• From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks
• Feedback Connections in Quantum Reservoir Computing with Mid-Circuit Measurements
• The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators
• Quantum reservoir computing for predicting and characterizing chaotic maps
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers