A quantum-classical hybrid branch & bound algorithm

TL;DR

This paper introduces a comprehensive quantum-classical hybrid branch-and-bound algorithm for binary linear programs, combining quantum optimization with classical techniques to improve solution quality and provide convergence guarantees.

Contribution

It presents a novel hybrid algorithm that integrates quantum optimization into classical branch-and-bound methods with convergence metrics and problem reduction strategies.

Findings

Demonstrates effectiveness on set partitioning problems

Provides detailed analysis of algorithm components

Achieves solution quality improvements

Abstract

We propose a complete quantum-classical hybrid branch-and-bound algorithm (QCBB) to solve binary linear programs with equality constraints. That includes bound calculation, convergence metrics and optimality guarantee to the quantum optimization based algorithm, which makes our method directly comparable to classical methods. Key aspects of the proposed algorithm are (i) encapsulation of the quantum optimization method, (ii) utilization of noisy samples for problem reduction, (iii) classical approximation based bound calculation, (iv) branch and bound traits like gap-based stopping criterion and monotonic increase in solution quality, (v) integrated composition of many different solutions that can be improved individually. We show numerical results on set partitioning problem instances and provide many details about the characteristics of the different steps of the algorithm.