Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting
A new arXiv preprint introduces a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, featuring two model variants: Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F). The framework explores complexity-fidelity trade-offs under near-term NISQ hardware constraints, using angle encoding and cross-channel entanglement to process multivariate data. Experiments on benchmark datasets show that VQF-F achieves superior training stability and parameter efficiency, while QRC-F demonstrates enhanced robustness and circuit fidelity under quantum noise.
Why it matters: This work presents a practical quantum-native approach to time-series forecasting, highlighting potential for real-world deployment on near-term quantum hardware and addressing key challenges in quantum machine learning for sequential data.
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