Quantum Computing Approaches for Mission Covering Optimization

2022/05/04 by Massimiliano Cutugno, Annarita Giani, Cutugno, Massimiliano +7
Computer Science · Physics and Astronomy · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2205.02212

openalex publication_date 2022/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study quantum computing algorithms for solving certain constrained resource allocation problems we coin as Mission Covering Optimization (MCO). We compare formulations of constrained optimization problems using Quantum Annealing techniques and the Quantum Alternating Operator Ansatz (Hadfield et al. arXiv:1709.03489v2, a generalized algorithm of the Quantum Approximate Optimization Algorithm, Farhi et al. arXiv:1411.4028v1) on D-Wave and IBM machines respectively using the following metrics: cost, timing, constraints held, and qubits used. We provide results from two different MCO scenarios and analyze results.

Citations

• A Quantum Approximate Optimization Algorithm

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