Convolutional Quantum Reservoir Computing for Time Series Forecasting: a case study on solar energy generation

Convolutional Quantum Reservoir Computing for Time Series Forecasting: a case study on solar energy generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Convolutional Quantum Reservoir Computing for Time Series Forecasting: a case study on solar energy generation Francesco Strata, Luca Migliori, Sara Pezzuolo, Cecilia Maria Ortoleva, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7273383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate forecasting of solar power generation is critical for maintaining grid stability, yet it remains a significant challenge due to the inherent volatility of solar resources. While classical machine learning models have advanced the field, they often struggle to capture the complex, non-linear dynamics underlying rapid generation changes. This paper introduces a novel hybrid framework, Convolutional Quantum Reservoir Computing (C-QRC), designed to enhance time series forecasting by creating rich, high-dimensional features. Our method employs a gate-based quantum circuit as a reservoir, which is convolved over input time series to augment the feature space for classical predictive models, including LSTM, Transformer, and XGBoost. We conduct a rigorous evaluation using a rolling-forecast methodology on three distinct datasets, comparing the performance of QRC-enhanced models against classical and baseline counterparts. Our results reveal that QRC does not act as a universal accuracy enhancer for point forecasts. Instead, its primary advantage lies in a specialized capability: the improved modeling of system dynamics, particularly during high-volatility periods and significant ramp events. We identify strong architectural synergies, where the QRC-LSTM combination excels at predicting system dynamics, while the QRC-Transformer pairing demonstrates superior robustness in the resulting point forecast. We conclude that QRC is not a general replacement for classical pre-processing but a strategic component for advanced hybrid systems, designed to mitigate critical failure modes by providing a more profound understanding of system dynamics. Quantum Computing Machine Learning Artificial Intelligence Quantum Machine Learning Energy Industry Convolutional Neural Networks Time Series Forecasting Reservoir Computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7273383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509134773,"identity":"9fd2b1b9-d4bd-42a9-af36-122de1b81964","order_by":0,"name":"Francesco 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