Computational Complexity of Quadratic Unconstrained Binary Optimization
Abstract
In this paper, we study the computational complexity of the quadratic unconstrained binary optimization (QUBO) problem under the functional problem FP^NP categorization. We focus on four sub-classes: (1) When all coefficients are integers QUBO is FP^NP-complete. (2) When every coefficient is an integer lower bounded by a constant k, QUBO is FP^NP[log]-complete. (3) When every coefficient is an integer upper bounded by a constant k, QUBO is again FP^NP[log]-complete. (4) When coefficients can only be in the set {1, 0, -1}, QUBO is FP^NP[log]-complete. With recent results in quantum annealing able to solve QUBO problems efficiently, our results provide a clear connection between quantum annealing algorithms and the FP^NP complexity class categorization. We also study the computational complexity of the decision version of the QUBO problem with integer coefficients. We prove that this problem is DP-complete.
Cite
@article{arxiv.2109.10048,
title = {Computational Complexity of Quadratic Unconstrained Binary Optimization},
author = {Hirotoshi Yasuoka},
journal= {arXiv preprint arXiv:2109.10048},
year = {2022}
}