Evaluating quantum circuits in the reservoir computing paradigm

• URL: http://arxiv.org/abs/2605.01253v1
• Date: Sat, 02 May 2026 05:25:27 GMT
• Title: Evaluating quantum circuits in the reservoir computing paradigm
• Abstract summary: Reservoir computing is a framework used for temporal information processing.<n>We study the effectiveness of a reservoir made using structured brickwall circuits built from two-qubit gates.
• Score: 0.5366467936613012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reservoir computing is a framework which is primarily used for temporal information processing, using the intrinsic dynamics of an underlying physical system. The framework, in a quantum setup, is implemented using ergodic dynamics associated with Hamiltonian models. The computational power of the reservoir is closely tied to this underlying dynamical nature, and to probe this further, we study the effectiveness of a reservoir that is made using structured brickwall circuits built from two-qubit gates. Here, the global ergodic nature of the circuit model results from the said arrangement, which has an important role in extracting useful performance with a minimal setup that is independent of an associated Hamiltonian. We focus on the nature of the gates used in this setup and evaluate the resulting reservoir performance, correlating the same with known results on the dynamical nature of the circuit in question. As a baseline, we analyse brickwall circuits composed of Haar-random two-qubit gates, before moving on to dual-unitaries, where tunable ergodic properties allow us to systematically investigate its relationship with reservoir performance. We further consider a class of non-random two-qubit gates obeying a specific solvability condition, wherein the associated dynamics surpasses the equivalent circuit made up of two qubit Haar random unitaries in terms of randomness. Finally, we consider examples of Krylov space analytics, which allow for a reliable prediction of effective circuit reservoirs for sufficient task performance. Using the introduced metrics we validate the reservoir for time-series prediction using standard synthetic data sets to evaluate the fading memory capacity and accuracy for prediction tasks. Our results indicate that structured quantum circuits would serve as effective models that yield better and efficient task performance in reservoir computing applications.

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