Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics

TL;DR

This paper introduces quantum reservoir probing (QRP), an inverse approach to quantum reservoir computing, which uses computational performance to explore and distinguish quantum many-body dynamics, especially in quantum Ising chains.

Contribution

The study develops QRP as a novel inverse method linking physical quantum properties to computational performance, enabling new insights into quantum many-body physics.

Findings

QRP distinguishes ballistic and diffusive information propagation.

QRP identifies system-specific information channels.

QRP offers a new tool for exploring quantum dynamics.

Abstract

Quantum reservoir computing (QRC) is a brain-inspired computational paradigm, exploiting natural dynamics of a quantum system for information processing. To date, a multitude of quantum systems have been utilized in the QRC, with diverse computational capabilities demonstrated accordingly. This study proposes a reciprocal research direction: probing quantum systems themselves through their information processing performance in the QRC framework. Building upon this concept, here we develop quantum reservoir probing (QRP), an inverse extension of the QRC. The QRP establishes an operator-level linkage between physical properties and performance in computing. A systematic scan of this correspondence reveals intrinsic quantum dynamics of the reservoir system from computational and informational perspectives. Unifying quantum information and quantum matter, the QRP holds great promise as a potent tool for exploring various aspects of quantum many-body physics. In this study, we specifically apply it to analyze information propagation in a one-dimensional quantum Ising chain. We demonstrate that the QRP not only distinguishes between ballistic and diffusive information propagation, reflecting the system's dynamical characteristics, but also identifies system-specific information propagation channels, a distinct advantage over conventional methods.