Dynamical Phase Transitions in Quantum Reservoir Computing

2021/03/31 by Rodrigo Martínez‐Peña, Rodrigo Martínez-Peña, Gian Luca Giorgi +3 · 3 citations
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Dynamical systems theory #Exploit #Neural Networks and Reservoir Computing #Nonlinear system #Phase transition #Physics #Quantum #Quantum Information and Cryptography #Quantum computer #Quantum information #Quantum many-body systems #Quantum mechanics #Recurrent neural network #Reservoir computing #Statistical physics #Theoretical computer science #Thermalisation #quant-ph

paper · pdf · doi:10.1103/physrevlett.127.100502

published as Phys. Rev. Lett. 127, 100502 (2021) · 14 pages, 9 figures

openalex publication_date 2021/08/31 · arxiv created 2021/09/06 · arxiv updated 2021/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Closed quantum systems exhibit different dynamical regimes, like many-body localization or thermalization, which determine the mechanisms of spread and processing of information. Here we address the impact of these dynamical phases in quantum reservoir computing, an unconventional computing paradigm recently extended into the quantum regime that exploits dynamical systems to solve nonlinear and temporal tasks. We establish that the thermal phase is naturally adapted to the requirements of quantum reservoir computing and report an increased performance at the thermalization transition for the studied tasks. Uncovering the underlying physical mechanisms behind optimal information processing capabilities of spin networks is essential for future experimental implementations and provides a new perspective on dynamical phases.

Citations

• Fate of Many-Body Localization Under Periodic Driving
• From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics
• Quantum annealing: The fastest route to quantum computation?
• The approach to thermal equilibrium in quantized chaotic systems
• Localization of interacting fermions at high temperature
• Quantum statistical mechanics in a closed system
• New results on recurrent network training: unifying the algorithms and accelerating convergence
• Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid
• Recent advances in physical reservoir computing: A review
• Quantum supremacy using a programmable superconducting processor
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Expressibility and trainability of parametrized analog quantum systems for machine learning applications
• Perturbative instability towards delocalization at phase transitions between MBL phases
• Do Reservoir Computers Work Best at the Edge of Chaos?

Cited by

• Estimation of the second-order coherence function using quantum reservoir and ensemble methods
• Statistical noise enhances quantumness benefits in spin-network quantum reservoir computing
• Predicting fermionic densities using a Projected Quantum Kernel method