Robust and Efficient Quantum Reservoir Computing with Discrete Time Crystal

TL;DR

This paper introduces a noise-robust quantum reservoir computing method using discrete time crystal dynamics, demonstrating improved classification accuracy and topological noise resilience on quantum processors, advancing quantum machine learning in the NISQ era.

Contribution

It presents the first experimental demonstration of quantum reservoir computing for image classification leveraging discrete time crystal dynamics, linking many-body physics with quantum machine learning performance.

Findings

Achieved quantum kernel advantage in binary classification.

Demonstrated topological noise robustness in experiments.

Showed improved accuracy with larger quantum systems.

Abstract

The rapid development of machine learning and quantum computing has placed quantum machine learning at the forefront of research. However, existing quantum machine learning algorithms based on quantum variational algorithms face challenges in trainability and noise robustness. In order to address these challenges, we introduce a gradient-free, noise-robust quantum reservoir computing algorithm that harnesses discrete time crystal dynamics as a reservoir. We first calibrate the memory, nonlinear, and information scrambling capacities of the quantum reservoir, revealing their correlation with dynamical phases and non-equilibrium phase transitions. We then apply the algorithm to the binary classification task and establish a comparative quantum kernel advantage. For ten-class classification, both noisy simulations and experimental results on superconducting quantum processors match ideal simulations, demonstrating the enhanced accuracy with increasing system size and confirming the topological noise robustness. Our work presents the first experimental demonstration of quantum reservoir computing for image classification based on digital quantum simulation. It establishes the correlation between quantum many-body non-equilibrium phase transitions and quantum machine learning performance, providing new design principles for quantum reservoir computing and broader quantum machine learning algorithms in the NISQ era.