Quantum Annealing for Complex Optimization in Satellite Communication Systems

Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.