A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917490036047872 |
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| author | Otieno, Wendy Zagoskin, Alexandre Balanov, Alexander G. Gongora, Juan Totero Savel'ev, Sergey E. |
| author_facet | Otieno, Wendy Zagoskin, Alexandre Balanov, Alexander G. Gongora, Juan Totero Savel'ev, Sergey E. |
| contents | We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding $86 \%$. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13094 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets Otieno, Wendy Zagoskin, Alexandre Balanov, Alexander G. Gongora, Juan Totero Savel'ev, Sergey E. Quantum Physics Data Analysis, Statistics and Probability We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding $86 \%$. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware. |
| title | A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets |
| topic | Quantum Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2602.13094 |