Hybrid photonic-quantum reservoir computing for time-series prediction

Abstract

Motivated by the perspective of advanced time-series prediction and exploitation of quantum reservoir computing (QRC), we explored the design and implementation of a hybrid photonic-QRC (HPQRC) paradigm. This brings together the high-speed parallelism of photonic systems with the quantum reservoir’s capacity to model complex, nonlinear dynamics, and hence acts as a powerful tool for performing prediction in a resource-constrained environment with low latency. We have engineered a solution using this architecture to address issues such as computational bottlenecks, energy inefficiency, and sensitivity to noise which are common in existing reservoir computing models. Our simulation results show that HPQRC consistently outperforms both classical and quantum-only reservoir models on chaotic, financial, and biomedical benchmarks: on Mackey–Glass and Lorenz systems, HPQRC reduces normalised mean squared error by 25.9% and 21.8% respectively over quantum-only RC; on MIT-BIH ECG R-peak prediction, HPQRC achieves 89.4% accuracy (within ±10 ms tolerance) compared to 81.3% for QRC; and on S&P 500 hourly direction prediction, HPQRC attains 55.13% mean directional accuracy versus 53.89% for QRC and 52.64% for Classical RC. The model retains above-chance mean predictive performance under 15% Gaussian noise (one-sample t(9)=5.54, p<0.001 versus 50% chance level). HPQRC also achieves a 56.1% reduction in per-prediction simulation wall-clock time relative to Classical RC, establishing it as a compelling simulation-validated paradigm for hybrid quantum-photonic reservoir computing.