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Edge computing is a promising technology that offers a superior userexperience and enables various innovative Internet of Things applications. Inthis paper, we present a mixed-integer linear programming (MILP) model foroptimal edge server placement and workload allocation, which is known to beNP-hard. To this end, we explore the possibility of addressing thiscomputationally challenging problem using quantum computing. However, existingquantum solvers are limited to solving unconstrained binary programmingproblems. To overcome this obstacle, we propose a hybrid quantum-classicalsolution that decomposes the original problem into a quadratic unconstrainedbinary optimization (QUBO) problem and a linear program (LP) subproblem. TheQUBO problem can be solved by a quantum solver, while the LP subproblem can besolved using traditional LP solvers. Our numerical experiments demonstrate thepracticality of leveraging quantum supremacy to solve complex optimizationproblems in edge computing.

Quantum-based Distributed Algorithms for Edge Node Placement and Workload Allocation