Energy optimization in AIoT-based healthcare monitoring using reinforcement learning variational quantum algorithm
Abstract
The integration of AIoT in healthcare allows continuous monitoring of patients and real-time clinical decision-making. Energy inefficiency of batteries in edge devices, as well as latency and accuracy limitations, are among the key concerns in the domain. In this study, a Reinforcement Learning (RL) driven Variational Quantum Algorithm (VQA)-based multi-objective approach (RL-VQA) to optimize AIoT for energy efficiency by incorporating quantum circuit parameter optimization is introduced. In this context, the intelligent optimization of the system-level design is attained through dynamic adjustment of variational parameters using reinforcement learning while considering energy efficiency, latency, and diagnostic accuracy. The problem setting is designed as a constrained optimization problem, allowing efficient search for possible solutions in the complex problem space through hybrid quantum-computational models. Results show that the approach yields up to 31.7% reduction in energy, 24 ms improvements in latency, and 1.1% higher accuracy in comparison to classical and static quantum approaches.
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