Hybrid Photonic-Quantum Reservoir Computing For Time-Series Prediction
Oishik Kar, Aswath Babu H
TL;DR
The paper addresses time-series forecasting by proposing Hybrid Photonic-Quantum Reservoir Computing (HPQRC), a architecture that combines fast photonic processing with quantum reservoir dynamics to achieve real-time prediction with low latency and improved accuracy. It details a methodology where amplitude-encoded time-series data interact with a 5-qubit superconducting quantum reservoir and a Kerr-nonlinear silicon nitride photonic reservoir, with outputs fused and refined via ridge regression. Empirical results on chaotic benchmarks and real-world datasets show HPQRC delivering higher predictive accuracy and substantially lower latency than classical or quantum-only baselines, with notable robustness to noise and high throughput. While promising for edge computing and complex dynamic systems, the work also notes practical deployment hurdles, including photonic integration challenges, decoherence management, and the need for standardized hardware and training protocols.
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-Quantum Reservoir Computing (HPQRC) paradigm. It brings together the high-speed parallelism of photonic systems with the quantum reservoir's capacity of modeling complex, nonlinear dynamics, and hence acts as a powerful tool for performing real-time prediction in resource resource-constrained environment with low latency. We have engineered a solution using this architecture to address issues like computational bottlenecks, energy inefficiency, and sensitivity to noise that are common in existing reservoir computing models. Our simulation results show that HPQRC attains much higher accuracy with lower computational time than both classical and quantum-only models. This model is robust when environments are noisy and scales well across large datasets, and therefore is suitable for application on diverse problems such as financial forecasting, industrial automation, and smart sensor networks. Our results substantiate that HPQRC performs significantly faster than traditional architectures and could be a viable and highly scalable platform for actual edge computing systems. Overall, HPQRC demonstrates significant advancements in time series modeling capabilities. In combination with enhanced predictive accuracy with reduced computational requirements, HPQRC establishes itself as an effective analytical tool for complex dynamic systems that require both precision and processing efficiency.