Leveraging Quantum Uncertainty for Modeling Financial Volatility via Quantum Reservoir Computing

Abstract Financial markets exhibit complex non-linear dynamics, temporal dependencies, and stochastic volatility, posing enduring challenges for classical forecasting models. Quantum computing provides a fundamentally different computational paradigm, exploiting superposition, entanglement, and quantum interference to encode and process information in richer representational spaces. Here, we introduce the Entangled Quantum Coherent Reservoir Network (eQCRN) — a quantum reservoir computing framework that leverages quantum uncertainty to model financial uncertainty. eQCRN integrates multiple quantum reservoirs with distinct entanglement configurations, enabling coherent feature expansion and improved temporal correlation modeling beyond classical limits. To our knowledge, this represents one of the first systematic evaluations of entangled quantum reservoirs on real-world financial data. Experiments on 30 stocks across the NASDAQ, S\&P 500, and FTSE indices show consistent performance improvements in volatile and non-linear markets, with eQCRN achieving 60.4\% accuracy on high-volatility technology stocks versus 58.2\% for the best classical models. This gain arises from representing market dynamics in quantum superposition states and capturing inter-temporal correlations via qubit entanglement. Ablation studies identify optimal configurations of 6--8 qubits and 10--12 reservoirs, balancing expressivity and noise resilience. These findings suggest that quantum reservoir computing can complement classical methods for modeling stochastic and non-stationary systems. While not yet a definitive demonstration of quantum advantage, the results indicate a promising pathway toward practical quantum-assisted learning for complex temporal-prediction tasks.