Quantum Optimization Benchmarking Library (QOBLIB)

The QOBLIB is an open-source resource designed to accelerate the search for quantum advantage in combinatorial optimization

QOBLIB introduces the “intractable decathlon,” a set of 10 challenging problem classes for benchmarking both quantum and classical optimization methods

The library aims to foster collaboration among researchers and practitioners to identify areas where quantum computers can outperform classical systems

Combinatorial optimization seeks the best solution from a finite set of possibilities, with real-world applications across science and industry

Most optimization algorithms are heuristics, making it difficult to predict their effectiveness on new problems and highlighting the need for extensive benchmarking

QOBLIB was developed by a consortium of researchers from leading institutions and companies worldwide

Benchmarking in QOBLIB is model-independent, focusing on application-level comparisons rather than system- or algorithm-specific metrics

To claim quantum advantage, a problem must be hard for all known classical methods and solvable more efficiently or accurately by quantum hardware and algorithms

The intractable decathlon includes problem classes that are both scientifically and practically relevant, and challenging even at small sizes for classical solvers

QOBLIB provides background, formal problem definitions, sample instances, and baseline results (classical and quantum) for each problem class