Deep Dive

Inhalte des Deep Dives zu Quantentechnologien als Enabler

Rund 20 Teilnehmende nutzten den Deep Dive, um sich umfassend über aktuelle Entwicklungen und Anwendungspotenziale im Bereich des Quantencomputings zu informieren. In einer offenen Atmosphäre wurden technologische Grundlagen erläutert, Anwendungsmöglichkeiten diskutiert und zahlreiche Zwischenfragen gestellt, was zu einem intensiven fachlichen Austausch führte. Besonders im Fokus standen potenzielle Einsatzfelder in der Automobilindustrie, Ansätze zur Quantenresilienz in der Fertigungsindustrie sowie das Zusammenspiel von Quantencomputing und künstlicher Intelligenz. Die entstandenen Diskussionen und Schlussfolgerungen unterstreichen die Relevanz von Quantentechnologien als eine der Schlüsseltechnologien des 21. Jahrhunderts.

Thematische Einblicke im Deep Dive

Mitmachangebote

Fazit

Der Deep Dive bot einen umfassenden Überblick über aktuelle Entwicklungen und Anwendungspotenziale der Quantentechnologie. Es wurde deutlich, dass sie als Schlüsseltechnologie einen wichtigen Beitrag zur Lösung gesellschaftlicher und industrieller Herausforderungen leisten kann und deshalb in der ARENA2036 gezielt gefördert wird.

Überblick

Kurzbiografien

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 laidthe foundation for quantum technological aspects he is now applying toquantum computing. Dr. Wurstbauer has authored more than a dozen researchpapers, 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 advancedcomposites. 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.