QUANTUM OPTIMIZATION FOR GAS SENSOR PLACEMENT ON OFFSHORE PLATFORMS VIA A WEIGHTED MAXIMUM COVERAGE MODEL

The positioning of gas sensors is important for the operational safety of offshore platforms, as it contributes to risk mitigation in critical areas. Defining the sensor configuration can be formulated as a combinatorial optimization problem. This study proposes modeling the sensor allocation problem as a Weighted Maximum Coverage Problem (WMCP), in which each risk area has an associated criticality level. The objective is to maximize weighted coverage using a limited number of sensors. To enable its solution through quantum algorithms, the formulation is converted into a Quadratic Unconstrained Binary Optimization (QUBO) model and solved using the Quantum Approximate Optimization Algorithm (QAOA), implemented on a quantum simulator. The approach is evaluated on an instance based on the Brazilian offshore platform MonoBR. The results indicate the potential of quantum optimization as a tool to support sensor placement decisions in offshore environments.