QuaRK: A Quantum Reservoir Kernel for Time Series Learning

TL;DR

QuaRK introduces a quantum reservoir kernel framework for time series learning, combining quantum dynamics with classical kernel methods, providing scalable, flexible, and theoretically grounded temporal data modeling.

Contribution

It presents the first end-to-end quantum reservoir kernel method with hardware-realistic features and theoretical guarantees for time series learning.

Findings

Empirical validation on synthetic time series demonstrates effective interpolation.

The framework offers explicit control over computational resources.

Theoretical guarantees link design choices to generalization performance.

Abstract

Quantum reservoir computing offers a promising route for time series learning by modelling sequential data via rich quantum dynamics while the only training required happens at the level of a lightweight classical readout. However, studies featuring efficient and implementable quantum reservoir architectures along with model learning guarantees remain scarce in the literature. To close this gap, we introduce QuaRK, an end-to-end framework that couples a hardware-realistic quantum reservoir featurizer with a kernel-based readout scheme. Given a sequence of sample points, the reservoir injects the points one after the other to yield a compact feature vector from efficiently measured k-local observables using classical shadow tomography, after which a classical kernel-based readout learns the target mapping with explicit regularization and fast optimization. The resulting pipeline exposes clear computational knobs -- circuit width and depth as well as the measurement budget -- while preserving the flexibility of kernel methods to model nonlinear temporal functionals and being scalable to high-dimensional data. We further provide learning-theoretic generalization guarantees for dependent temporal data, linking design and resource choices to finite-sample performance, thereby offering principled guidance for building reliable temporal learners. Empirical experiments validate QuaRK and illustrate the predicted interpolation and generalization behaviours on synthetic beta-mixing time series tasks.