Quantum Reservoir Computing for Statistical Classification in a Superconducting Quantum Circuit

TL;DR

This paper demonstrates that quantum reservoir computing implemented in superconducting circuits can effectively classify complex statistical distributions and time series, outperforming classical methods especially with limited data, showing promise for real-world applications.

Contribution

It introduces a superconducting circuit-based quantum reservoir computing model capable of complex statistical classification, highlighting its advantages over classical approaches.

Findings

QRC accurately classifies heavy-tailed distributions

QRC identifies regimes in correlated time series

QRC outperforms classical methods with limited data

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.