Quantum-Classical Hybrid Optimization Algorithms for Logistics and Production Line Management
The QCHALLenge project solves optimization problems in production and logistics using existing quantum computing (QC) hardware. For this purpose, algorithms, concepts and tools are developed that enable the industry to use QC in a cross-industry and low-threshold manner. QCHALLenge focuses on the domains of production and logistics due to their key role for the German economy. This results in use cases such as the optimization of supply chains and warehouses as well as the use of QC in automation. Implementations are primarily in hybrid (quantum-classical) form and are designed in such a way that potential customers can quickly benefit from a quantum advantage without having to deal with the technology of QC.
Challenge and innovation
In the prevailing NISQ era (i.e., limited to current, comparatively small and error-prone quantum hardware), one of the biggest challenges for QCHALLenge is to identify use cases with potential quantum benefits. In particular, issues of scaling, taking into account developments in QC hardware, are central to this. In addition, the stated goal of hardware agnosticism poses a considerable hurdle from a technical point of view due to the very diverse software interfaces of hardware manufacturers. Based on these and other difficulties in the implementation of QC solutions for application-relevant problems, QCHALLenge brings numerous innovations into practice: (1) The development of generic development tools and environments, (2) The creation of user-friendly, engineering applications such as optimizations for specific use cases. (3) The development of software solutions for the integration of conventional computers and QC systems (hybrid quantum software), and (4) The design of strategies and methods for the structured analysis of application-side problems with respect to the targeted application and development of QC solutions.
QCHALLenge focuses on the integration of QC into existing software workflows to solve application problems, mainly in the area of optimization. In particular, QCHALLenge also aims at the optimization of methods in machine learning and simulation. For this purpose, suitable interfaces are identified and implemented on the basis of industrial use cases, which enable the profitable use of QC solutions. Central to the practical implementation is the access to QC hardware, whereby the optimal QC hardware is selected for each problem and solution set. In addition, QC solutions are implemented that enable the immediate use of QC without a deeper understanding of the technology and development tools are developed that facilitate the implementation of QC solutions. The final result of the project is a comprehensive prototype that enables the low-threshold integration of QC into existing software solutions after a thorough evaluation and revision according to requirements. As a by-product, recommendations for action for the industry and user-friendly QC applications for practice-relevant use cases are created in the course of the project, the use of which does not require a deeper understanding of QC in order to promote the targeted development of QC solutions.
Ludwig-Maximilians-Universität München, BASF SE, BMW AG, SAP SE, Siemens AG
July 2022 – June 2025
Total cost: € 7.1 million
Funding volume: € 4.3 million