Adiabatic Quantum Computing for Solving the Weapon-Target Assignment Problem

TL;DR

This paper explores using adiabatic quantum computing to solve the NP-hard weapon-target assignment problem, demonstrating potential for broader optimization applications through numerical simulation of quantum evolution.

Contribution

It introduces a quantum computing approach for solving a complex optimization problem, showing feasibility via numerical simulation and potential adaptability to other allocation tasks.

Findings

Numerical simulation shows quantum approach can find optimal solutions.

Method is adaptable to other resource allocation problems.

Quantum model demonstrates potential for practical optimization applications.

Abstract

Quantum computing promises significant improvements of computation capabilities in various fields such as machine learning and complex optimization problems. Recent technological advancements suggest that the adiabatic quantum computing ansatz may soon see practical applications. In this work, we adopt this computation paradigm to develop a quantum computation based solver of the well-known weapon target assignment problem, an NP-hard nonlinear integer programming optimization task. The feasibility of the presented model is demonstrated by numerical simulation of the adiabatic evolution of a system of quantum bits towards the optimal solution encoded in the model Hamiltonian. Over all, the described method is not limited to the context of weapon management but is, with slight modifications to the model Hamiltonian, applicable to worker-task allocation optimization in general.