Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat, Ali Habibnia
arXiv 20 May 2025 · quant-ph · publishedPhysical Review Research (2026) · 1 citations (OpenAlex)
arXiv:2505.13933 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | author author A. Bucci,\ title title Realized Volatility Forecasting… (2020) ) NoStop | 0.874 | 8 | 2 | 100% |
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| 3 | author author K. Christensen, author M. Siggaard,\ and\ author B. Ve… ( year 2023) NoStop | 0.737 | 3 | 2 | 100% |
| 4 | author author J. Garcá-Beni, author G. L.\ Giorgi, author M. C.\ Sor… (2023) ) NoStop | 0.737 | 3 | 2 | 100% |
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Showing the top 10 of 134 scored citations.