Bench-QC – Application-driven benchmarking of quantum computers

The hopes in quantum technologies are high. For example, quantum computing has the potential to change many industries through the high computing capacity and the opportunities that result. In particular, quantum computing is expected to significantly help solve simulation problems and complex optimization problems.

However, current quantum computers such as the Noisy Intermediate-Scale Quantum (NISQ) computers have not yet been able to solve these problems better than conventional methods and conventional high-performance computers. However, the development of QC hardware is progressing rapidly and a practical quantum advantage could already result in the next few years.

When does quantum computing pay off for industry?

So far, however, there are no systematic, scientific analyzes of the potential of quantum computers in practical applications. So the benefits and the use for the industry are still unclear. This is to change with the joint project Bench-QC.

The goal of the Bench-QC project is to investigate when quantum computers produce better results than classic high-performance computers. Only then will quantum computing become interesting for industrial use. For this purpose, the six project partners rely on systematic application-driven benchmarking of quantum computing. This is used to evaluate which quantitative and qualitative characteristics quantum computers have to fulfill in order to be able to exploit the advantages for different applications. An open question is, for example: How many qubits in which quality do you need for which applications? A special focus is placed on simulation problems, optimization issues and quantum machine learning.

The Bench-QC project is a lighthouse project of the Munich Quantum Valley of the Bavarian State Government, on which six project partners from science and industry work together. It is funded by the Bavarian State Ministry of Economic Affairs, Regional Development and Energy with EUR 1.6 million within the framework of the High-Tech Agenda Plus.

Benchmarking procedure for quantum computing in practice

Development of benchmarks from use cases

In order to be able to reliably assess the advantage of quantum computing for practical applications, the Bench-QC project evaluates when an advantage over classic solutions might be apparent, with a view to specific applications.

For this, benchmarks are developed in several steps from concrete use cases:

  1. Use case: First, different industrial use cases with increasing complexity are defined. These use cases come from the fields of machine learning, optimization and simulation.
  2. Mathematical formulation: The mathematical formulation is then developed therefrom.
  3. Problem size and complexity: Based on this, the problem sizes and complexities are defined for each use case.
  4. Disassembly into classic and QC parts: The resulting problem sequences with different complexity are broken down into hybrid algorithms, which means that parts of the problem are solved with the help of quantum computing, but others still on classic systems.
  5. Implementation on QC hardware or in simulation: The algorithms that are to be solved with quantum computing are then implemented on different QC hardware or a corresponding simulation. These QC-based solutions are also compared to classic solutions, which is why a special focus is on developing suitable metrics for this comparison.

Benchmarking for machine learning use cases

Within the framework of the Bench-QC project, Fraunhofer IKS is particularly involved in developing and implementing the benchmarking procedure for use cases in the field of machine learning. In addition to the above-mentioned steps, suitable metrics and complexity measures for machine learning archetypes such as Supervised, Unsupervised and Reinforcement Learning and the corresponding use cases must be defined for this purpose. For these tasks, Fraunhofer IKS brings its experience from previous projects, in particular its competence in the realization of robust quantum machine learning.

The project is being sponsored by the Bavarian Ministry of Economic Affairs, Regional Development and Energy (StMWi).

More information about quantum computing


Munich Quantum Valley

Munich Quantum Valley conducts research on the industrial use of quantum computers and quantum technologies. To ensure that quantum computing can be used safely, Fraunhofer IKS contributes its expertise on the reliable application of advanced technologies in safety-critical systems.


Quantum Computing

Quantum computers could enable new applications. Fraunhofer IKS is researching safe software applications for quantum computing so that the calculations can be relied on.