@inproceedings{WinkerGroppeUotilaetal., author = {Winker, Tobias and Groppe, Sven and Uotila, Valter Johan Edvard and Yan, Zhengtong and Lu, Jiaheng and Maja, Franz and Mauerer, Wolfgang}, title = {Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research}, series = {SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA}, booktitle = {SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA}, publisher = {ACM}, address = {New York}, doi = {10.1145/3555041.3589404}, pages = {8}, abstract = {In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms havethe proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of join order problem for databases.}, language = {en} } @inproceedings{UotilaGroppeGruenwaldetal., author = {Uotila, Valter and Groppe, Sven and Gruenwald, Le and Lu, Jiaheng and Mauerer, Wolfgang}, title = {Preface QDSM}, series = {Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) — International Workshop on Quantum Data Science and Management (QDSM'23), August 28 - September 1, 2023, Vancouver, Canada (CEUR Workshop Proceedings)}, booktitle = {Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) — International Workshop on Quantum Data Science and Management (QDSM'23), August 28 - September 1, 2023, Vancouver, Canada (CEUR Workshop Proceedings)}, publisher = {RWTH Aachen, Sun SITE Central Europe}, address = {Aachen}, abstract = {The first international workshop on Quantum Data Science and anagement (QDSM), co-located with VLDB 2023, is centered around addressing the possibilities of quantum computing for data science and data management. Quantum computing is a relatively new and emerging field that is believed to have huge computational potential in the future. In the QDSM workshop, we want to provide a venue for discussing and publishing novel results of applying quantum computing to hard data science and data management problems. These problems include join order optimization, designing efficient quantum feature maps, studying possibilities of solving linear programs with quantum algorithms, and divergent index tuning with quantum machine learning. Besides, we include a short and visionary survey on quantum computing for databases. Theworkshop provides a platform for active discussion on these and related topics.}, language = {en} }