Efficient Hamiltonian Reduction for Quantum Annealing on SatCom Beam Placement Problem

TL;DR

This paper introduces an efficient Hamiltonian Reduction method enabling quantum annealers to solve large Beam Placement problems in satellite communication, demonstrating significant improvements over existing solutions on real quantum hardware.

Contribution

The paper presents a novel Hamiltonian Reduction technique that allows quantum annealers to handle larger BP instances, advancing quantum optimization applications in satellite systems.

Findings

Quantum annealers can solve 17.5 times larger BP instances.

The proposed method outperforms existing solutions on real quantum hardware.

Early quantum computing applications in satellite optimization are demonstrated.

Abstract

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.