Hybrid quantum-classical scheduling optimization in UAV-enabled IoT networks

This work investigates a scenario in which a swarm of unmanned aerial vehicles serves a set of sensor nodes, adopting the time division multiple access scheme. To ensure fair resource allocation and derive an optimal scheduling plan, a combinatorial problem subject to binary constraints is formulated. Thanks to its inherent capabilities, quantum annealing can be used to solve this class of optimization problems. As a result, the original problem is mapped to quadratic unconstrained binary optimization form, in order to be processed by a quantum processing unit. Since state-of-the-art quantum annealers have a limited number of quantum bits (qubits) and limited inter-qubit connectivity, the scheduling plan is obtained by employing a hybrid quantum-classical approach. Then, a comparison with two classical solvers is performed in terms of acquired data, objective function values, and execution time.