QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems

TL;DR

QOPTLib is a new benchmark dataset designed for evaluating quantum computing approaches on combinatorial optimization problems, including instances of TSP, VRP, Bin Packing, and Max Cut, with initial quantum annealing solutions provided.

Contribution

This paper introduces QOPTLib, the first comprehensive quantum computing-oriented benchmark for multiple combinatorial problems, facilitating standardized evaluation and comparison.

Findings

First full solutions using quantum annealing on QOPTLib

Provides a baseline for future quantum algorithms

Includes diverse problem instances for broad testing

Abstract

In this paper, we propose a quantum computing oriented benchmark for combinatorial optimization. This benchmark, coined as QOPTLib, is composed of 40 instances equally distributed over four well-known problems: Traveling Salesman Problem, Vehicle Routing Problem, one-dimensional Bin Packing Problem and the Maximum Cut Problem. The sizes of the instances in QOPTLib not only correspond to computationally addressable sizes, but also to the maximum length approachable with non-zero likelihood of getting a good result. In this regard, it is important to highlight that hybrid approaches are also taken into consideration. Thus, this benchmark constitutes the first effort to provide users a general-purpose dataset. Also in this paper, we introduce a first full solving of QOPTLib using two solvers based on quantum annealing. Our main intention with this is to establish a preliminary baseline, hoping to inspire other researchers to beat these outcomes with newly proposed quantum-based algorithms.