SIGMOD 2023 Tutorial

Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research

In recent years, quantum computing has experienced remarkable progress. The progress has been rapid in both hardware and software fields. The prototypes of quantum computers already exist and have been made available to users through cloud services. Although fault-tolerant, large-scale quantum computers do not yet exist, the potential of quantum computing technology is undeniable. Quantum algorithms have a proven ability to either outperform the corresponding classical algorithms or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even noisy intermediate-scale quantum computing technologies are speculated to exhibit computational advantages over classical systems.

One of the most promising approaches to possibly demonstrate this advantage is quantum machine learning. Meanwhile, the database community has successfully applied various machine learning algorithms for data management tasks, so combining the fields appears promising. However, quantum machine learning is a new 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 optimize the join order problem for databases.

The tutorial is planned for 3 hours and will have the following structure.

  • Introduction and motivation (10 min). We introduce the background and remarkable progress of quantum computing. We show a correlation exists between the availability of larger quantum computers and the publication performances of researchers in the areas of quantum computing and quantum machine learning.
  • Basics of quantum computing (50 min). We present the basics of quantum computing techniques, including quantum bits, Bloch sphere, and multi-qubit states.
  • Quantum machine learning (50 min). We introduce quantum machine learning techniques, including hybrid quantum-classical algorithms, variational quantum circuits, encoding, and decoding.
  • Break and QA (20 min). We allocate time to answer the questions and encourage interaction with the audience. 
  • Demo about join order optimization with quantum machine learning (30 min). We design a demo to demonstrate using quantum machine learning to optimize join order with the framework Qiskit.
  • Open problems and challenges for database research (20 min). We discuss the state-of-the-art and open challenges to applying quantum machine learning for database research.

Link to demo Github:




Tobias Winker is a Ph.D. student at the University of Lübeck with a master's degree in computer science. He is a member of the QC4DB (Quantum Computing for Databases) project. His research interests are classical and quantum machine learning for database problems.

Sven Groppe is a Professor at the University of Lübeck and the project coordinator of the QC4DB project. His research includes integrating quantum computers as accelerators into DBMS and high-level quantum programming languages. He is also the chair of SBD@SIGMOD (2016-2020), BiDEDE@SIGMOD (2021-2023), and VLIoT@VLDB (2017-2022).

Valter Uotila is a Ph.D. student in the Unified Database Management Systems research group at the University of Helsinki. He received a master's degree in mathematics. His research interests are in the intersection of databases, quantum computing, and category theory.

Zhengtong Yan is a Ph.D. student at the University of Helsinki. His research topics lie in quantum computing and reinforcement learning for databases.

Jiaheng Lu is a Professor at the University of Helsinki, Finland.  His current research interests focus on multi-model databases and quantum computing for database applications.  He has written four books on  Hadoop and NoSQL databases and more than 130 journal and conference papers published in SIGMOD, VLDB, TODS, etc.

Maja Franz is a research master's student at the Technical University of Applied Sciences Regensburg. Her research interests focus on quantum algorithms for near-term quantum devices, especially in industrially relevant domains. After her master's, she intends to obtain a Ph.D. degree in the field of quantum computing.

Wolfgang Mauerer is a Professor at the Technical University of Applied Sciences Regensburg, and a Senior Research Scientist at Siemens Technology. His interests focus on software/systems engineering and quantum computing. He has published strongly multi-disciplinary work in venues and journals from Nature Photonics and PRL via ICSE and TSE to SIGMOD.