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  3. The Quantum Optimization Benchmarking Library.
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Related Experiment Video

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

The Quantum Optimization Benchmarking Library.

Thorsten Koch1,2, David E Bernal Neira3, Ying Chen4

  • 1Zuse Institute Berlin, Berlin, Germany. koch@zib.de.

Nature Computational Science
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new framework for benchmarking quantum optimization algorithms using ten diverse problem classes. It provides standardized benchmarks to track progress toward quantum advantage in combinatorial optimization.

Related Experiment Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Quantum Computing
  • Combinatorial Optimization
  • Algorithm Benchmarking

Background:

  • Benchmarking heuristic quantum algorithms at scale is becoming feasible.
  • Empirical analysis is crucial for tracking progress towards quantum advantage in optimization.

Purpose of the Study:

  • To introduce a systematic, fair, and comparable benchmarking framework for quantum optimization methods.
  • To present ten model-independent problem classes challenging for classical methods.

Main Methods:

  • Developed ten problem classes with varying properties, challenging classical methods with up to 100,000 decision variables.
  • Created an open-source repository for track records of specific instances and solutions.
  • Referenced state-of-the-art classical solver results and demonstrated exemplary quantum solver baselines.

Main Results:

  • The framework enables standardized benchmark reporting for quantum optimization.
  • Problem instances span a range of difficulty, from under 100 to 100,000 decision variables.
  • Exemplary baseline results with quantum solvers are provided for selected problems.

Conclusions:

  • The presented framework facilitates rigorous comparison of quantum and classical optimization algorithms.
  • Standardized benchmarks are essential for driving the field towards demonstrable quantum advantage.
  • The open-source repository supports reproducible research and community engagement.