Deep Dive

Contents of the deep dive on quantum technologies as enablers

Around 20 participants took advantage of the Deep Dive to find out all about current developments and potential applications in the field of quantum computing.In an open atmosphere, technological basics were explained, possible applications were discussed and numerous questions were asked, which led to an intensive professional exchange. The focus was particularly on potential fields of application in the automotive industry, approaches to quantum resilience in the manufacturing industry and the interplay between quantum computing and artificial intelligence. The resulting discussions and conclusions underline the relevance of quantum technologies as one of the key technologies of the 21st century.

Thematic insights in the Deep Dive

Participation offers

Conclusion

The deep dive provided a comprehensive overview of current developments and potential applications of quantum technology. It became clear that, as a key technology, it can make an important contribution to solving social and industrial challenges and is therefore being specifically promoted in ARENA2036.

Overview

Short biographies

Lars Hohmuth After receiving a physics degree from Ulm University in Germany, Lars Hohmuth worked for technology companies in UK, USA, Japan and Germany in fields ranging from mathematical software, distributed device management, IOT and large-scale traffic simulations to quantum computing. He is currently serving as Managing Director of QuiX Quantum GmbH.

Dr. Sławomir Folwarski - Partner Architect in DXC Technology. Member of Automotive AI Center of Excellence in the DXC Data & AI Practice (and Quantum Practice), where he focuses on data workload optimization and analytics platforms architecture. Sławomir has over 20 years of experience in the automotive, telco, public sector, logistics and finance industries, with expertise in data warehousing, business intelligence and hadoop/big data technologies implemented on premises in cloud and hybrid. He teaches and evangelizes QC technology contributing to events like Airbus BMW QC Challenge, DXC Innovation Week and Technothon. Since Jan 2023 he has been pursuing his doctorate degree in quantum computing at Capitol Technology University. Has also fulfilled IBM Quantum Challenge Fall 2022 Achievement.

Dr. Ulrich Wurstbauer is an innovation strategist and technological leaderfocused on autonomous driving and quantum computing. As DXC Distinguished Technologist and global Chief Technologist for AutonomousDriving, he is continuously pushing the frontiers of current real-world industrychallenges and future R&D demands. As senior member of the IEEE, Dr. Wurstbauer holds a doctorate in Physics from the University of Regensburg and has done post-doctoral studies at the physics department of the Columbia University in the City of New York. His academic education and research laid the foundation for quantum technological aspects he is now applying to quantum computing. Dr. Wurstbauer has authored more than a dozen research papers, has filed four national and international patent applications and has(co-) authored several thought leadership blogs.

Dr. - Ing Muhammad Saeed is a Research Coordinator in ARENA2036. He is a researcher and engineer specializing in mechanical engineering, simulation technology, and intelligent systems. Holding a dual Ph.D. from the University of Stuttgart and Swinburne University, hiswork focuses on integrating classical AI, AutoML, and Quantum Machine Learning (QML) into industrial applications. With expertise in CAE, automation, digital twin and robotics, he develops methodological and scalable frameworks that bridge digital engineering with next-generation industry 5.0, enabling predictive control, simulations, optimization, and smart manufacturing across domains such as automotive, smart factories, and advanced composites. He is also actively engaged in aligning AI innovations with emerging ethicalguidelines and regulatory frameworks to ensure responsible and compliant deployment in industry 4.0. In his presentation he talks about the next generation of industrial AI needs scalable, intelligent, and automated systems. He shows how we can improve AutoML by using Quantum Machine Learning (QML) to solve problems in real-world situations like production, automotive, and simulations. By combining the efficiency of classical machine learning with the advanced capabilities ofquantum models, such as quantum kernels and quantum neural networks (QNNs), AutoQMLallows us to explore complex solution spaces and automate the creation of hybrid ML workflows.