Hybrid Quantum-Classical Heuristic for the Bin Packing Problem

TL;DR

This paper introduces a hybrid quantum-classical algorithm for the one-dimensional Bin Packing Problem, combining quantum subroutines for feasible configurations with classical methods for solution construction, demonstrating its validity through benchmarking.

Contribution

It presents a novel hybrid approach (H-BPP) that integrates quantum and classical algorithms specifically for 1dBPP, addressing a key challenge in quantum optimization.

Findings

H-BPP effectively finds feasible bin configurations.

The hybrid method shows promising performance on benchmark instances.

Quantum subroutine contributes to solution feasibility analysis.

Abstract

Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.