Feedback-driven quantum reservoir computing for time-series analysis

Abstract

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 element in time-series processing. Notably, analysis of measurement trajectories reveal 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.

15 pages, 10 figures

References in corpus (43)

• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Variational Quantum Algorithms
• Barren plateaus in quantum neural network training landscapes
• Recent Advances in Physical Reservoir Computing: A Review
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Quantum advantage in learning from experiments
• Quantum Data Fitting
• Hybrid quantum-classical algorithms and quantum error mitigation
• Strong and weak thermalization of infinite non-integrable quantum systems
• Harnessing disordered quantum dynamics for machine learning
• Provably efficient machine learning for quantum many-body problems
• Quantum reservoir processing
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• Quantum reservoir computing with a single nonlinear oscillator
• Dynamical phase transitions in quantum reservoir computing
• Quantum neuromorphic computing
• Temporal Information Processing on Noisy Quantum Computers
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Natural quantum reservoir computing for temporal information processing
• Do Reservoir Computers Work Best at the Edge of Chaos?
• Computational Capabilities of Random Automata Networks for Reservoir Computing
• Hybrid quantum-classical reservoir computing of thermal convection flow
• Learning temporal data with variational quantum recurrent neural network
• Scalable photonic platform for real-time quantum reservoir computing
• Realising and compressing quantum circuits with quantum reservoir computing
• Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
• The Reservoir Learning Power across Quantum Many-Boby Localization Transition
• Quantum reservoir computation utilising scale-free networks
• 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
• Benchmarking the role of particle statistics in Quantum Reservoir Computing
• Retrieving past quantum features with deep hybrid classical-quantum reservoir computing
• Impact of the form of weighted networks on the quantum extreme reservoir computation
• Estimating the randomness of quantum circuit ensembles up to 50 qubits
• Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets
• Video recognition by physical reservoir computing in magnetic materials

Cited by in corpus (17)

• Harnessing Quantum Extreme Learning Machines for image classification
• Practical Few-Atom Quantum Reservoir Computing
• Quantum reservoir computing on random regular graphs
• Turbulent mesoscale convection in the Boussinesq limit and beyond
• Input-dependence in quantum reservoir computing
• Effects of dissipation in reservoir computing using a spin qubit array
• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins
• Optimal quantum reservoir learning in proximity to universality
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
• From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
• Feedback Connections in Quantum Reservoir Computing with Mid-Circuit Measurements
• Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
• Quantum reservoir computing for predicting and characterizing chaotic maps
• Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers