Quantum-Classical Hybrid Optimization Algorithms for Logistics and Production Line Management (QCHALLenge)

The LMU Munich acts as consortium leader in the project and is responsible for a large part of the project management. As a research institution with many years of expertise in quantum computing (e.g., LMU is a founding partner of PlanQK), LMU provides support in all research-intensive work packages. As a university research group, the QAR-Lab of the LMU plans to apply the project results in the context of teaching, in scientific publications and in funding and research projects based on QCHALLenge.

SAP, as a leading provider of business application software, represents on the one hand the user side regarding optimization problems from business and industry and on the other hand provides its expertise in the field of design, architecture, and development of software for large industrial customers. In this role, SAP also leads work packages in which use cases from the areas of optimization, ML & simulation, as well as materials research are implemented using a hybrid approach (quantum-classical algorithms) and then compared with purely classical methods. Different implementations are evaluated with respect to their performance, scalability, extensibility, etc., coordinated with each other, and then incorporated into the design of an optimal software architecture.

The focus of Siemens‘ work in the QCHALLenge project is on integrating quantum computing (QC) methods into existing automation solutions as seamlessly as possible. This integration concerns different phases in the life cycle of an automation solution, e.g. the configuration of an optimization problem within an automation, the interfaces between automation solution and QC software or reconfiguration and software updates. Appropriate concepts and tools will be developed for this purpose in the project’s work complexes. In the medium to long term, the aim is to commercialize the concepts and solutions as part of Siemens’ automation portfolio.

BASF is participating in the joint project QCHALLenge as an industrial partner and has the role and perspective of an end user of quantum computing in business, in this case, of the chemical industry. BASF already uses mathematical optimization together with machine learning in ongoing business operations in R&D, product development, process development, production and logistics. Continuously finding more efficient solutions to these optimization tasks not only makes sense for BASF from an economic perspective, but also corresponds to the overarching values of resource savings and sustainability. The overall goal of BASF’s subproject within QCHALLenge correlates with this strategy: The project aims to develop a qualitative assessment of the business impact of quantum computing for industry.

BWM also participates in the joint project QCHALLenge as an industrial partner and brings the insights of an end user of quantum computing in the economy to the consortium. As a major company in the automotive industry, BMW is confronted with various optimization tasks in the areas of production and logistics during ongoing business operations. Continuously finding more efficient solutions to these optimization tasks not only makes sense for BMW from an economic perspective, but also corresponds to the overarching values of resource conservation and sustainability.

As a subcontractor for LMU and BASF, AQARIOS contributes its QC know-how and knowledge from current customer projects with DAX corporations. The main focus is the development of QC software tools. The goal is to enable the low-threshold use of QC in an industrial environment; by generalizing QC solution approaches for problems from production and logistics as well as defining and implementing suitable interfaces for industrial use, QC software tools that can be used in industry are to be created.