Optimizing Sensor Subset Selection with Quantum Annealing: A Large-Scale Indoor Temperature Regulation Application

Harnessing the potential of quantum computing, we apply D-Wave quantum annealing to tackle the Sensor Subset Selection Optimization (SSSO) problem within the scope of large-scale temperature regulation. Leaning on the principles of superposition and entanglement, the study navigates expansive combinatorial spaces of sensor subsets, showcasing impressive scalability and consistently high-quality solutions. A distinctive hybrid approach, merging classical computation for precomputing Mean Squared Errors (MSEs) and quantum computation to explore vast optimization spaces, is introduced. Approaches like these contribute to the field of Intelligent Human-Computer Interaction by enabling smart environment control, effectively optimizing user interaction with their surroundings and enhancing their overall experience. Results affirm the efficiency of our quantum model across varying complexities, producing solutions that rank within the top 91.43 percentile of potential outcomes. Beyond sensor networks, these methods can influence broader human-computer interaction dynamics. Future research will address real-time MSE calculation and objective function enhancement to increase their robustness, along with experimentation on more comprehensive sensor networks.