Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing

• URL: http://arxiv.org/abs/2506.17547v1
• Date: Sat, 21 Jun 2025 02:33:49 GMT
• Title: Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Authors: Kaito Kobayashi, Yukitoshi Motome,
• Abstract summary: In reservoir computing, optimal performance is typically achieved at the edge of chaos," the boundary between order and chaos.<n>Here, we identify its quantum many-body counterpart using the QRC implemented on the celebrated Sachdev-Ye-Kitaev model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reservoir computing (RC) is a machine learning paradigm that harnesses dynamical systems as computational resources. In its quantum extension -- quantum reservoir computing (QRC) -- these principles are applied to quantum systems, whose rich dynamics broadens the landscape of information processing. In classical RC, optimal performance is typically achieved at the ``edge of chaos," the boundary between order and chaos. Here, we identify its quantum many-body counterpart using the QRC implemented on the celebrated Sachdev-Ye-Kitaev model. Our analysis reveals substantial performance enhancements near two distinct characteristic ``edges": a temporal boundary defined by the Thouless time, beyond which system dynamics is described by random matrix theory, and a parametric boundary governing the transition from integrable to chaotic regimes. These findings establish the ``edge of many-body quantum chaos" as a fundamental design principle for QRC.

Related papers

• Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability [3.7960472831772765]
  We show that quantum reservoir computers are robust tools for learning and forecasting chaotic dynamics from time-series data.<n>This work opens opportunities for designing robust quantum machines for chaotic time series forecasting on near-term quantum hardware.
  arXiv  Detail & Related papers  (2025-06-27T15:42:20Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [48.670876200492415]
  Variational Quantum Algorithms (VQAs) are a promising approach for leveraging powerful Noisy Intermediate-Scale Quantum (NISQ) computers.<n>We propose $rho$DARTS, a differentiable Quantum Architecture Search (QAS) algorithm that models the search process as the evolution of a quantum mixed state.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Multi-reference Quantum Davidson Algorithm for Quantum Dynamics [3.3869539907606603]
  Quantum Krylov Subspace (QKS) methods have been developed, enhancing the ability to perform accelerated simulations on noisy intermediate-scale quantum computers. We introduce and evaluate two QKS methods derived from the QDavidson algorithm, a novel approach for determining the ground and excited states of many-body systems.
  arXiv  Detail & Related papers  (2024-06-12T22:30:52Z)
• Solving reaction dynamics with quantum computing algorithms [42.408991654684876]
  We study quantum algorithms for response functions, relevant for describing different reactions governed by linear response.<n>We focus on nuclear-physics applications and consider a qubit-efficient mapping on the lattice, which can efficiently represent the large volumes required for realistic scattering simulations.
  arXiv  Detail & Related papers  (2024-03-30T00:21:46Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics [0.0]
  This study proposes a reciprocal research direction: probing quantum systems themselves through their information processing performance. Building upon this concept, here we develop quantum reservoir probing (QRP), an inverse extension of the Quantum Reservoir Computing (QRC) paradigm. Unifying quantum information and quantum matter, the QRP holds great promise as a potent tool for exploring various aspects of quantum many-body physics.
  arXiv  Detail & Related papers  (2023-08-02T01:26:36Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Impact of the form of weighted networks on the quantum extreme reservoir computation [0.0]
  The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model. We show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance.
  arXiv  Detail & Related papers  (2022-11-15T01:50:47Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Higher-Order Quantum Reservoir Computing [0.0]
  We propose a hybrid quantum-classical framework consisting of multiple but small quantum systems that are mutually communicated via classical connections like linear feedback. We demonstrate the effectiveness of our framework in emulating large-scale nonlinear dynamical systems.
  arXiv  Detail & Related papers  (2020-06-16T08:54:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.