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Launch of QOBLIB-Quantum Optimization Benchmarking Library

The Quantum Optimization Benchmarking Library (QOBLIB) attempts to accelerate quantum advantage discovery.

The Quantum Optimisation Benchmarking Library (QOBLIB) by the Quantum Optimisation Working Group is a key step towards understanding and achieving quantum advantage in combinatorial optimisation. This new open-source repository and article provide the “intractable decathlon,” 10 carefully selected problem classes. A challenging setting for testing quantum and traditional optimisation approaches is its goal. The program encourages researchers and practitioners to collaborate to speed up development and identify areas where quantum computers can outperform classical computers for real-world problems.

Combinatorial optimisation seeks the optimal option from a limited set. These methods are necessary for tackling many important scientific and industrial problems. Finding the best answer for many real-world optimisation problems is still difficult. Therefore, many common quantum and classical optimisation methods are heuristics, which use intuition-based “rules of thumb” to tackle complicated problems.

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Heuristic algorithms cannot guarantee performance in advance, making it difficult to predict which difficulties they will solve. Finding examples of quantum advantage requires extensive testing on real quantum hardware using tough, practically important difficulties, even though these systems can often offer “good enough” answers for real-world use cases.

Researchers from over a dozen member organisations, including Zuse Institute Berlin, Technische Universität Berlin, Purdue University, National University of Singapore, E.ON Digital Technology GmbH, Kipu Quantum GmbH, Forschungszentrum Jülich, University of Southern California, and IBM Quantum, created QOBLIB to provide a centralised resource and collaborative environment.

Due to the number and complexity of optimisation problems and solution techniques, no single researcher or company can conduct this rigorous testing. QOBLIB aims to use the working group’s and optimisation research community’s knowledge.

Benchmarking for Advantage Benchmarking is used in computing to evaluate a fixed algorithm on a system, improve algorithms and understand their scalability, or decide whether a quantum or conventional approach is best for an application.

Applications, algorithms, and systems benchmarking are shown. System and algorithm benchmarking can be useful even if it is model-dependent and specialised to a model or approach, but applications benchmarking must be model-independent. The pursuit for quantum advantage is expected to hinge on applications benchmarking.

Finally, benchmarking must consider all methods for defining and solving an issue to prove an advantage.

System and technique evaluation has dominated optimisation benchmarking, which is often model-dependent. New QOBLIB paper and library give model-independent benchmarks for optimisation applications to close this gap. Though challenging to construct model-agnostic benchmarks because to the wide variety and complexity of optimisation problem classes and their solution methodologies, it is considered an essential impediment to quantum advantage.

The Quantum Advantage State that quantum advantage claims in any application domain must meet two requirements:

There must be difficult hurdles in all classical techniques. Quantum computers are useless if classical computers can deliver good, affordable answers. Quantum hardware and algorithms must prove they can tackle the problem better, faster, or cheaper than classical alternatives. Also Utimaco Quantum Protect Uses Post-Quantum Cryptography

It is rare to find optimisation issues that meet both requirements. Unsolved optimization problems that are both practically and scientifically relevant are rarely disclosed; instead, academics tend to simplify these problems in order to find “good enough” answers for certain use cases. However, the quality of the solution is sometimes sacrificed in the name of simplification. The practical impact of optimization could be greatly increased by being able to address more complicated issues with fewer simplifications.

The Intractable Decathlon is now available The “intractable decathlon” a grouping of ten issue classes is the focal point of the new project. According to the working group, this collection is the first collection of optimization problems that are both scientifically and practically intriguing and that, even at relatively small problem sizes, become challenging for the most advanced classical solvers. These issues were also chosen because they are appropriate for investigation on near-term quantum devices, which still have qubit count and circuit depth restrictions despite advancements in technology.

Even while these particular challenges might not be the ones that ultimately yield quantum advantage in combinatorial optimization, they do offer a clear indication of possible areas for quantum advantage. The project offers precise, well-defined measures to make it easier to find an advantage and allow for equitable comparisons of all kinds of quantum and classical approaches.

To assist researchers in getting started and comparing performance, each problem class in the decathlon is provided with background data, a formal problem formulation, descriptions of particular problem instances that are available in QOBLIB, and traditional baseline findings. For certain issues, quantum baseline findings are also shown.

The QOBLIB Repository: A Platform for Collaboration The Quantum Optimization Benchmarking Library is set up as a publicly available, open-source database. In order to cover typically tough situations, it includes problem instances for every problem class, varying in size and complexity. This makes it possible for researchers to monitor hardware and algorithmic developments leading to quantum advantage.

QOBLIB provides a submission template with explicit metrics to guarantee fair comparisons. The quality of the answer obtained, the overall amount of wall clock time, and the total amount of computational resources used both classical and quantum are some examples of these measures. Reference models are also available in the repository. These comprise quadratic unconstrained binary optimization (QUBO) formulas as well as mixed-integer programming (MIP). For classical researchers, MIP frequently acts as an entry point, whereas QUBO does the same for quantum researchers. These models enable researchers to examine the performance of quantum algorithms in conjunction with the classical baseline data.

It is stressed that neither MIP nor QUBO are optimal; rather, they are just samples of how issues can be formulated. They are offered as places for researchers to start their investigation. Model complexity may rise as a result of mapping MIP to QUBO formulations, which may result in an increase in the number of variables, problem density, and coefficient ranges. Scholars are urged to draw inspiration from these in order to create whole new formulations that may be more appropriate for quantum processing or even allow for superior classical solutions.

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An Appeal for Action Quantum advantage is thought to have very serious promise in optimization. The unsolvable decathlon is a significant advancement, but achieving its full potential calls for cooperation. No one entity can finish this voyage by itself due to the enormous amount of challenges and algorithms to investigate.

This community effort specifically calls for and encourages participation from researchers and practitioners with expertise in quantum and classical optimization techniques. Researchers can directly contribute to a project that may result in the first demonstrations of quantum advantage by evaluating performance, testing new and existing algorithms against the QOBLIB challenges, and uploading results to the repository. It is also essential to continuously build new and enhanced models and algorithms.

In order to solve important problems that are now beyond the scope of classical methods alone, the goal is for everyone to work together to propel mathematical optimization into a new era of computation.

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