QRC-Lab: An Educational Toolbox for Quantum Reservoir Computing

• URL: http://arxiv.org/abs/2602.03522v1
• Date: Tue, 03 Feb 2026 13:38:56 GMT
• Title: QRC-Lab: An Educational Toolbox for Quantum Reservoir Computing
• Authors: Anderson Fernandes Pereira dos Santos,
• Abstract summary: Quantum Reservoir Computing (QRC) has emerged as a strong pa- radigm for Noisy Intermediate-Scale Quantum (NISQ) machine learning.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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