QRC-Lab: An Educational Toolbox for Quantum Reservoir Computing

TL;DR

QRC-Lab is an open-source Python toolkit that facilitates the study and teaching of Quantum Reservoir Computing, enabling exploration of quantum dynamics and machine learning applications through configurable experiments and educational case studies.

Contribution

This work introduces QRC-Lab, a modular framework that bridges quantum dynamics and machine learning, with formal definitions, configurable experiments, and educational case studies.

Findings

Validated reservoir concepts through case studies

Demonstrated quantum reservoir computing for temporal data tasks

Provided tools for studying encoding, connectivity, and measurement strategies

Abstract

Quantum Reservoir Computing (QRC) has emerged as a strong pa- radigm for Noisy Intermediate-Scale Quantum (NISQ) machine learning, ena- bling the processing of temporal data with minimal training overhead by exploi- ting the high-dimensional dynamics of quantum states. This paper introduces QRC-Lab, an open-source, modular Python framework designed to bridge the gap between theoretical quantum dynamics and applied machine learning work- flows. We provide a rigorous definition of QRC, contrast physical and gate- based approaches, and formalize the reservoir mapping used in the toolbox. QRC-Lab instantiates a configurable gate-based laboratory for studying in- put encoding, reservoir connectivity, and measurement strategies, and validates these concepts through three educational case studies: short-term memory re- construction, temporal parity (XOR), and NARMA10 forecasting as a deliberate stress test. In addition, we include a learning-theory motivated generalization- gap scan to build intuition about capacity control in quantum feature maps. The full source code, experiment scripts, and reproducibility assets are publicly available at: https://doi.org/10.5281/zenodo.18469026.