Quantum Computing Approaches for Mission Covering Optimization

TL;DR

This paper explores quantum computing algorithms, specifically Quantum Annealing and the Quantum Alternating Operator Ansatz, to solve constrained resource allocation problems called Mission Covering Optimization, comparing their performance on D-Wave and IBM quantum machines.

Contribution

It introduces the application of quantum algorithms to MCO problems and compares their effectiveness and resource usage on different quantum hardware.

Findings

Quantum algorithms can solve MCO problems with varying efficiency.

Different quantum hardware shows distinct performance characteristics.

Quantum approaches hold promise for complex resource allocation tasks.

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.