Work

Quantum Reservoir Computing for Statistical Classification in a Superconducting Quantum Circuit

Year: 2026

Type: preprint

Abstract: We analyze numerically the performance of Quantum Reservoir Computing (QRC) for statistical and financial problems. We use a reservoir composed of two superconducting islands coupled via their charge degrees of freedom. The key non-linear elements that provide the reservoir with rich and complex dynamics are the Josephson junctions that connect each island to the ground. We show that QRC implemented in this circuit can accurately classify complex probability distributions, including those with heavy tails, and identify regimes in correlated time series, such as periods of high volatility generated by standard econometric models. We find QRC to outperform some of the best classical methods when the amount of information is limited. This demonstrates its potential to be a noise-resilient quantum learning approach capable of tackling real-world problems within currently available superconducting platforms. We further discuss how to improve our QRC algorithm in real superconducting hardware to benefit from a much larger Hilbert space.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2602.15474

Authors: Prieto-Garcia, J. J., del Pozo-Martín, A. G., Pino, M.

Institution: Universidad de Salamanca

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Ministerio de Ciencia, Innovación y Universidades, University of Warwick, European Commission, European Regional Development Fund, Agencia Estatal de Investigación

Awards: G2262748287, G3480869486, G3681454997, G3763328530, G4042783231, G425668148, G451917667, G5967599077, G7084143925, G7863371703, G8497050386

OpenAlex ID: W7130314007

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