Quantum Computing Approaches for Mission Covering Optimization
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.
Massimiliano Cutugno Annarita Giani Paul M. Alsing Laura Wessing Austars Schnore