TY - CHAP A1 - Winker, Tobias A1 - Groppe, Sven A1 - Uotila, Valter Johan Edvard A1 - Yan, Zhengtong A1 - Lu, Jiaheng A1 - Maja, Franz A1 - Mauerer, Wolfgang T1 - Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research T2 - SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA N2 - 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. Y1 - 2023 U6 - https://doi.org/10.1145/3555041.3589404 PB - ACM CY - New York ER - TY - CHAP A1 - Uotila, Valter A1 - Groppe, Sven A1 - Gruenwald, Le A1 - Lu, Jiaheng A1 - Mauerer, Wolfgang T1 - Preface QDSM T2 - 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) N2 - 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. Y1 - 2023 UR - https://ceur-ws.org/Vol-3462/QDSM1.pdf PB - RWTH Aachen, Sun SITE Central Europe CY - Aachen ER - TY - CHAP A1 - Franz, Maja A1 - Winker, Tobias A1 - Groppe, Sven A1 - Mauerer, Wolfgang T1 - Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation T2 - 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada N2 - Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-76877 N1 - Corresponding author der OTH Regensburg: Maja Franz SP - 409 EP - 420 PB - IEEE ER -