Quantum Speedups for Group Relaxations of Integer Linear Programs

TL;DR

This paper introduces quantum algorithms for solving group relaxations of ILPs, achieving super-quadratic speedups and improving solution bounds, thus advancing quantum optimization methods for discrete problems.

Contribution

It develops quantum algorithms for Gomory's group relaxation of ILPs, providing super-quadratic speedups and practical improvements in solving and bounding ILPs.

Findings

Quantum algorithms achieve super-quadratic speedups for ILP group relaxations.

Classical local-search algorithms effectively solve the group relaxation.

Numerical results show improved bounds and reduced integrality gaps in practical ILPs.

Abstract

Integer Linear Programs (ILPs) are a flexible and ubiquitous model for discrete optimization problems. Solving ILPs is \textsf{NP-Hard} yet of great practical importance. Super-quadratic quantum speedups for ILPs have been difficult to obtain because classical algorithms for many-constraint ILPs are global and exhaustive, whereas quantum frameworks that offer super-quadratic speedup exploit local structure of the objective and feasible set. We address this via quantum algorithms for Gomory's group relaxation. The group relaxation of an ILP is obtained by dropping nonnegativity on variables that are positive in the optimal solution of the linear programming (LP) relaxation, while retaining integrality of the decision variables. We present a competitive feasibility-preserving classical local-search algorithm for the group relaxation, and a corresponding quantum algorithm that, under reasonable technical conditions, achieves a super-quadratic speedup. When the group relaxation satisfies a nondegeneracy condition analogous to, but stronger than, LP non-degeneracy, our approach yields the optimal solution to the original ILP. Otherwise, the group relaxation tightens bounds on the optimal objective value of the ILP, and can improve downstream branch-and-cut by reducing the integrality gap; we numerically observe this on several practically relevant ILPs. To achieve these results, we derive efficiently constructible constraint-preserving mixers for the group relaxation with favorable spectral properties, which are of independent interest.