mlsanityData to 14 Oct 2026

Coherence influx is indispensable for quantum reservoir computing

arXiv 2409.12693 ↗First posted 19 Sep 2024quant-ph math.DS stat.MLcited by 3

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Abstract (arXiv)

Echo state property (ESP) is a fundamental property that allows an input-driven dynamical system to perform information processing tasks. Recently, extensions of ESP to potentially nonstationary systems and subsystems, that is, nonstationary ESP and subset/subspace ESP, have been proposed. In this paper, we theoretically and numerically analyze the sufficient and necessary conditions for a quantum system to satisfy nonstationary ESP and subset/subspace nonstationary ESP. Based on extensive usage of the Pauli transfer matrix (PTM) form, we find that (1) the interaction with a quantum-coherent environment, termed textitcoherence influx, is indispensable in realizing nonstationary ESP, and (2) the spectral radius of PTM can characterize the fading memory property of quantum reservoir computing (QRC). Our numerical experiment, involving a system with a Hamiltonian that entails a spin-glass/many-body localization phase, reveals that the spectral radius of PTM can describe the dynamical phase transition intrinsic to such a system. To comprehensively understand the mechanisms under ESP of QRC, we propose a simplified model, multiplicative reservoir computing (mRC), which is a reservoir computing (RC) system with a one-dimensional multiplicative input. Theoretically and numerically, we show that the parameters corresponding to the spectral radius and coherence influx in mRC directly correlates with its linear memory capacity (MC). Our findings about QRC and mRC will provide a theoretical aspect of PTM and the input multiplicativity of QRC. The results will lead to a better understanding of QRC and information processing in open quantum systems.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 17 references that are in the map. First posted on arXiv 19 Sep 2024.

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2 of 17 references.

References · 2

  1. 2023On fundamental aspects of quantum extreme learning machinescited by 18
  2. 2023Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirscited by 16

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Citing papers per year: 2025: 2; 2026: 1.

3 papers.