Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Thomas Hubregtsen1, Christoph Segler2, Josef Pichlmeier3, Aritra Sarkar4, Thomas Gabor3, Koen Bertels4
1BMW Research, 2BMW Group Research, New Technologies, Innovations, 3Ludwig Maximilian University of Munich, 4Delft University of Technology


Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed to algorithmic research on arti cial data that is disconnected from live systems, such as optimization of sys- tems or training of learning algorithms. In this paper we in- vestigate the integration of quantum systems into industry- grade system architectures. In this work we propose a sys- tem architecture for the integration of quantum accelera- tors. In order to evaluate our proposed system architec- ture we implemented various algorithms including a classi- cal system, a gate-based quantum accelerator and a quan- tum annealer. This algorithm automates user habits us- ing data-driven functions trained on real-world data. This also includes an evaluation of the quantum enhanced ker- nel, that previously was only evaluated on arti cial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of- the-art kernel. We also showed a low reduction in accu- racy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We, therefore, conclude it is feasible to integrate NISQ-era de- vices in industry-grade system architecture in preparation for future hardware improvements.