QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems
Eneko Osaba, Esther Villar-Rodriguez
TL;DR
QOPTLib tackles the lack of standardized benchmarks for quantum optimization by presenting 40 instances across four classical problems (TSP, VRP, 1dBPP, MCP) chosen to be solvable on current quantum hardware and scalable via hybrid methods. The authors provide baseline results by solving the instances with a pure quantum processor (DWAVE Advantage) and with a quantum-classical hybrid (LeapHybridBQMSampler), showing that hybrid approaches extend solvable problem sizes on the benchmark. This benchmark aims to enable fair comparisons, reproducibility, and the rapid development of quantum algorithms by supplying a shared dataset and initial performance baselines. The work highlights directions for expanding the benchmark to additional problems and developing new quantum solvers, ultimately advancing practical quantum optimization research.
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.