The department of computing science at the University of Helsinki is taking action on developing quantum computing-based algorithms and solutions. As a part of the development, Valter Uotila from the UDBMS research group and Sardana Ivanova from the computational creativity research group participated in the BMW Group's Quantum Computing Challenge.
Valter and Sardana worked as a team called "Qumpula Quantum" and qualified for the finals in the challenge. The team considered the BMW's challenge as a great opportunity to establish connections to the industry, get familiar with the current quantum computing trends in the industry, and learn to develop quantum computing-based solutions. Although the challenge was strongly car engineering-oriented, the same quantum computing principles and ideas can be utilized widely in the other areas of computer science. The team believes that the same methods can be applied in database optimization and computational creativity challenges.
In Autumn 2021 the automotive industry corporate BMW organized a challenge to crowd-source quantum computing innovations for various combinatorically difficult optimization challenges in collaboration with Amazon. The topics were "optimization of sensor positions for automated driving functions", "simulation of material deformation in the production process", "optimization of pre-production vehicle configuration" and "machine learning for automated quality assessment". Amazon offered a significant amount of cloud credits for the finalists to take advantage of Amazon's quantum computing services in Amazon Braket.
Valter and Sardana developed and implemented a prototype system for the optimization of sensor positions for automated driving functions. Their solution is based on a special kind of quantum computing called quantum annealing. They aimed to minimize the total costs of the positioned sensors while maximizing their environment coverage and minimizing their overlap.
The competition consisted of two rounds where the team Qumpula Quantum reached the second round and pitched their solution against companies like Accenture and 1QBit. Eventually, Accenture was selected as the winner of the sensor positions optimization track.
You can find more details about the solution from the pitch slides and from the implementation in Github. In the future, the team is planning to develop the idea further and publish the results in a relevant quantum computing venue.