A six-qubit quantum reservoir achieves over 86% accuracy in classifying stock trend movements for quantum-sector companies using daily and intraday volume data.
A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets
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abstract
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.
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A parametrized four-qubit digital QRC model with ridge-regression readout matches the classical Prophet benchmark on dynamic time warping for ATM cash-demand forecasting but underperforms on MAE and NMSE across noiseless simulation, noise emulation, and real IQM hardware.
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A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets
A six-qubit quantum reservoir achieves over 86% accuracy in classifying stock trend movements for quantum-sector companies using daily and intraday volume data.
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Digital Quantum Reservoir Computing for ATM Time Series Prediction
A parametrized four-qubit digital QRC model with ridge-regression readout matches the classical Prophet benchmark on dynamic time warping for ATM cash-demand forecasting but underperforms on MAE and NMSE across noiseless simulation, noise emulation, and real IQM hardware.