A Novel Hybrid Quantum-Classical Path Optimization for Methane Detection Using Remote Quantum Intensity Prediction Models

Mobile methane leak monitoring and detection systems may be constrained by the time required to reach the leak location and the energy needed throughout the process. In this paper, a physics-aware hybrid quantum-classical architecture is developed combined with classical methods with a quantum design. The proposed approach uses environmental factors to calculate leak locations and areas of higher concentration. The output is provided to a self-guided drone, which detects possible leak sources within its battery and within environmental constraints. In simulated tests, the proposed approach found leaks faster and used less energy per confirmed leak than traditional/classical methods, while meeting the same flight-time and power constraints.