arXiv:2603.08072v2 Announce Type: replace Abstract: Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes th
The convergence of advanced AI with quantum computing research is accelerating, as evidenced by efforts to integrate quantum components into established machine learning architectures for specialized tasks.
This work represents a step forward in leveraging quantum computing's potential for critical applications like healthcare, especially in predictive diagnostics, potentially leading to more accurate and timely interventions.
The proposed hybrid quantum-classical architecture could enable more precise and multi-horizon forecasting of complex physiological data, enhancing medical monitoring capabilities.
- · Healthcare sector
- · Quantum computing companies
- · AI research institutions
- · Medical device manufacturers
- · Traditional diagnostic methods (potentially)
- · Legacy medical data analysis systems
Improved early detection of patient crises through more accurate physiological signal forecasting.
Increased demand for quantum-aware AI specialists and quantum hardware tailored for medical applications.
Ethical considerations surrounding the deployment of highly predictive, quantum-enhanced AI in life-critical medical scenarios.
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