The Reservoir Learning Power across Quantum Many-Boby Localization Transition

Abstract

Harnessing the quantum computation power of the present noisy-intermediate-size-quantum devices has received tremendous interest in the last few years. Here we study the learning power of a one-dimensional long-range randomly-coupled quantum spin chain, within the framework of reservoir computing. In time sequence learning tasks, we find the system in the quantum many-body localized (MBL) phase holds long-term memory, which can be attributed to the emergent local integrals of motion. On the other hand, MBL phase does not provide sufficient nonlinearity in learning highly-nonlinear time sequences, which we show in a parity check task. This is reversed in the quantum ergodic phase, which provides sufficient nonlinearity but compromises memory capacity. In a complex learning task of Mackey-Glass prediction that requires both sufficient memory capacity and nonlinearity, we find optimal learning performance near the MBL-to-ergodic transition. This leads to a guiding principle of quantum reservoir engineering at the edge of quantum ergodicity reaching optimal learning power for generic complex reservoir learning tasks. Our theoretical finding can be readily tested with present experiments.

arXiv comment6 pages, 5 figures

References in corpus (41)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A bound on chaos
• Quantum computational advantage using photons
• Many-body localization, thermalization, and entanglement
• Black holes and the butterfly effect
• Superconducting Qubits: Current State of Play
• Integrated Photonic Quantum Technologies
• NMR Techniques for Quantum Control and Computation
• Silicon Quantum Electronics
• Observation of a Discrete Time Crystal
• Many-Body Physics with Individually-Controlled Rydberg Atoms
• Unbounded growth of entanglement in models of many-body localization
• Phenomenology of fully many-body-localized systems
• A phenomenology of certain many-body-localized systems
• Local conservation laws and the structure of the many-body localized states
• Many-body localization in a quantum simulator with programmable random disorder
• Photonic quantum information processing: a review
• Universal slow growth of entanglement in interacting strongly disordered systems
• Quantum Simulators: Architectures and Opportunities
• Demonstration of the trapped-ion quantum-CCD computer architecture
• Integrals of motion in the Many-Body localized phase
• Perspectives of quantum annealing: Methods and implementations
• Physical reservoir computing -- An introductory perspective
• Quantum simulation and computing with Rydberg-interacting qubits
• Harnessing disordered quantum dynamics for machine learning
• Constructing local integrals of motion in the many-body localized phase
• Quantum Computer Systems for Scientific Discovery
• A concise review of Rydberg atom based quantum computation and quantum simulation
• Operator growth in the SYK model
• Interferometric probes of many-body localization
• Quantum reservoir processing
• Dynamical phase transitions in quantum reservoir computing
• Temporal Information Processing on Noisy Quantum Computers
• Efficient implementation of selective recoupling in heteronuclear spin systems using Hadamard matrices
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Programmable Quantum Annealing Architectures with Ising Quantum Wires
• Many-body localization in spin chains with the long-range transverse interactions: scaling of critical disorder with the system size
• Design of time delayed chaotic circuit with threshold controller
• Quantum Information Scrambling Through a High-Complexity Operator Mapping

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