Quantum Reservoir Computing for Realized Volatility Forecasting

[2505.13933] Quantum Reservoir Computing for Realized Volatility Forecasting Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2505.13933 (quant-ph) [Submitted on 20 May 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: Quantum Reservoir Computing for Realized Volatility Forecasting Authors: Qingyu Li , Chiranjib Mukhopadhyay , Abolfazl Bayat , Ali Habibnia View a PDF of the paper titled Quantum Reservoir Computing for Realized Volatility Forecasting, by Qingyu Li and 3 other authors View PDF HTML (experimental) Abstract: Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of quantum reservoir computing for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The quantum reservoir computing approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets, and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof-of-concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance. Comments: 24 pages, close to published version Subjects: Quantum Physics (quant-ph) ; Econometrics (econ.EM); Statistical Finance (q-fin.ST) Cite as: arXiv:2505.13933 [quant-ph] (or arXiv:2505.13933v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2505.13933 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Physical Review Research 8, 023028 (2026) Related DOI : https://doi.org/10.1103/rbj7-4wnq Focus to learn more DOI(s) linking to related resources Submission history From: Chiranjib Mukhopadhyay [ view email ] [v1] Tue, 20 May 2025 05:02:13 UTC (3,948 KB) [v2] Thu, 9 Apr 2026 06:35:54 UTC (4,643 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Reservoir Computing for Realized Volatility Forecasting, by Qingyu Li and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-05 Change to browse by: econ econ.EM q-fin q-fin.ST References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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