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Q(AI)2: Quantum Artificial Intelligence for the Automotive Industry

Tobias Stollenwerk; Somtapa Bhattacharya; Michele Cattelan; Alessandro Ciani; Gabriele Compostella; David Headley; Johannes Klepsch; Matthias Klusch; Markus Leder; Antonio Macaluso; Kristel Michielsen; Dmytro Nabok; Anestis Papanikolaou; Alexander Rausch; Marco Schumann; Andrea Skolik; Sheir Yarkoni; Frank K. Wilhelm
In: Matthias Klusch; Jörg Lässig; Frank Wilhelm (Hrsg.). KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI), Vol. 4/24, Pages 1-9, Springer, 2024.

Abstract

No Abstract: Project Report. Introduction: The goal of the project Q(AI)2 was to acquire a broader basis of quantum computing enhanced AI and optimization algorithms for potential applications in the automotive industry. In particular, we strove to assess the potential for quantum advantage for specific use cases from the automotive industry. For this, we developed quantum algorithmic solutions tailored both to the available hardware as well as to the industrial problems at hand. In addition, we ensured industrial relevance of the investigated applications by the constitution of the consortium, involving all three large German car manufactures VW, Mercedes-Benz and BMW as well as Bosch as a central component supplier. Forschungszentrum Jülich and the German Center for Artificial Intelligence (DFKI) provided the necessary expertise in AI and quantum computing methods. In this work we will report on the results of the above approach and provide an outlook to the future of quantum-accelerated AI applications in the automotive sector. We begin by reporting on our result on quantum supervised learning methods with a particular focus on quantum kernel methods in Sect. 2 applied to use cases from computational engineering and quality assurance. Next, we cover quantum reinforcement learning approaches applied to collision-free navigation of self-driving cars in Sect. 3, before we turn to the solution of planning and scheduling problems from the automotive industry, like flexible job-shop scheduling and ride-pooling with quantum optimization methods in Sect. 4. Lastly, we report on our findings concerning benchmarking use-cases and algorithms with a focus on the restrictions imposed by the inevitable noise of real quantum computer hardware on variational quantum algorithms in Sect. 5, before we conclude with a summary of our main lessons learned in the project.