International Workshop on Quantum Data Science and Management (QDSM)

Abstract

The first international workshop on Quantum Data Science and Management (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 eficient 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. The workshop provides a platform for active discussion on these and related topics.

1. Introduction

of data science and data management technologies. Our purpose is to foster the interaction between database The field of quantum computing has experienced re- researchers and more traditional quantum disciplines, markable progress after decades of research and devel- as well as industrial users. The workshop serves as a opment. Prototypes of quantum computers already exist forum for the growing quantum computing community and have been made available to users through cloud to connect with database researchers to discuss the wider services (e.g., IBM Q experience, Google Quantum AI, questions and applications of how quantum resources or Xanadu Cloud). Although large-scale fault-tolerant can benefit data science and data management tasks, and quantum computers are not available yet, the potential how quantum software can support this endeavor. of this new technology is undeniable. Quantum algo- We believe that many unsolved and interesting issues rithms have the proven ability to either outperform clas- can be found at boundaries and intersections between sical approaches for various tasks or are impossible to diferent fields and that there are insuficient venues to be eficiently simulated by classical means under reason- publish such cross-disciplinary results. We also believe able complexity-theoretic assumptions. Even imperfect that an important aspect of future quantum computing current-day technology is speculated to exhibit compu- will concern issues of handling data in one way or another. tational advantages over classical systems. The workshop Quantum Data Science and Management

For most database researchers, quantum computing will serve as a venue not only to discuss early, experimenand quantum machine learning are still new research tal results in research but also to feature a demonstraifelds. The goal of this workshop is to bring together tion part with the intention of providing attendees with academic researchers and industry practitioners from ifrst-hand experience in using novel quantum computing multiple disciplines (e.g., database, AI, software, physics, techniques that go beyond the simple examples ofered etc.) to discuss the challenges, solutions, and applications by various web services. This will give researchers a realof quantum computing and quantum machine learning istic intuition about quantum computing for data science that have the potential to advance the state of the art and data management tasks. frameworks for enabling quantum data science and man- The articles have been evaluated according to the folagement. lowing aspects:

Experiments and Analysis Papers focus on the ex- • Relevance to the workshop perimental evaluation of existing approaches, including • Novelty and practical impact data structures and algorithms for quantum data science • Technical soundness and management, and bring new insights through the • Appropriateness and adequacy of literature review, analysis of these experiments. Results of Experiments background discussion, and analysis of issues and Analysis Papers can be, for example, showing the • Presentation, including overall organization, English, benefits of well-known approaches in new settings and and readability environments, opening new research problems by demonstrating unexpected behavior or phenomena or comparing a set of traditional approaches in an experimental 5. Rationale about Recruiting the survey. Chairs and Program Committee

Application Papers report practical experiences on with special regard to Diversity applications of quantum data science and management.

Application Papers might describe how to apply quantum Considerations technologies to specific application domains.

Vision Papers identify emerging new or future re- The PC chairs of the Quantum Data Science and Managesearch issues and directions and describe new research ment workshop are coming from two continents, Europe visions for quantum data science and management. The and North America, which would attract an international new visions will potentially have significant impacts on community. One of the PC chairs is female (25%). The society. h-index of the PC chairs ranges between 19 and 401.

Demo Papers deal with innovative approaches and We have currently recruited 20 PC members (inclusive applications for quantum data science and management. chairs) listed in the previous section who are experts in These papers describe a showcase of the proposed ap- the topics of interest of our proposed workshop. All PC proach/application. We are especially interested in members have already confirmed their membership. Our demonstrations having a WOW efect. PC represents a good mixture of diferent experiences, not only in terms of research areas but also in terms of levels of research experience. Although most PC mem3. Topics of Interest bers are from academia, we also have two experts from the industry and one expert from a national research We are interested in all topics concerning quantum com- laboratory. The chairs and PC members are listed in the puting for data science and management, such as the Appendix. following: • Quantum computing, quantum algorithms and quantum software tools for problems related to data science 6. Accepted Papers and management • Quantum machine learning for data science, data man- The accepted papers include four research papers and agement and database optimization one short survey paper. • Post-quantum cryptography and security for databases Quantum Optimisation of General Join Trees [ 1 ] by and data management Scönberger, Trummer, and Mauerer is a continuation of • Classical data science and management for quantum their previous work on optimizing left-deep join trees uscomputing and quantum machine learning ing quantum computing [ 2 ]. In this work, they introduce a native QUBO encoding for the general join ordering problem, which selects an optimal plan among bushy

4. Review process join trees.

An Evolutionary Algorithm Design for Pauli-based Quantum Kernel Classification [ 3 ] by Tjandra and Sugiarto tackles the problem of designing a high-performance quantum feature map that could work as an alternative kernel for Support Vector Classifiers. They represent a method to automatically generate Pauli feature maps using the genetic algorithm. Based on the results of their evaluation, the Pauli feature maps generated by We have enforced a rigorous peer and single-anonymous review process with the option for authors of a doubleanonymous review process. All manuscripts submitted to our workshop have been reviewed by at least three PC members. To verify the originality of submissions, we have used Plagiarism Detection Tools to check the content of the submitted manuscripts against previous publications.

1according to Google Scholar and Scopus

the genetic algorithm perform better than several other the corresponding classical algorithms, we are still posclassical and quantum kernel baselines. itive about the future of quantum computing. We are

Empirical evaluation of a quantum accelerated approach convinced that it is the right moment to start researchfor the central path method in linear programming [ 4 ] ing quantum computing possibilities for data science by Adoni, Hafshejani and Gaur study if the central path and data management. We are looking forward to momethod benefits from replacing the equation-solving step tivating, insightful, and enthusiastic discussions at the with the quantum algorithm for linear systems of equa- workshop. The submitted papers and the expertise of tions (HHL-algorithm). They use numerical simulations the keynote speakers and the authors are among the and multiple instances of the proposed algorithm to eval- first to propose quantum computing solutions to data uate the efectiveness of the quantum computing-based science and data management problems. We are conapproach. ifdent that our workshop will foster the collaboration

Index Tuning with Machine Learning on Quantum Com- of researchers and practitioners and support networkputers for Large-Scale Database Applications [ 5 ] by Gru- ing for long-lasting professional connections after the enwald, Winker, Çalıkyılmaz, Groppe, and Groppe pro- workshop. vides a vision of a quantum machine learning algorithm to optimize the divergent design index tuning problem for replicated databases to minimize query processing References costs. They outline the previous work and discuss the challenges and issues of designing such a quantum algorithm.

Quantum Computing for Databases: A Short Survey and Vision [ 6 ] by Yuan, Lu, Chen, Wu, Yao, Yan, and Chen represents a short survey and a visionary outline for the future of applying quantum computing for data management and databases.

7. Proceedings

We publish the accepted articles in a joint workshop proceedings called Proceedings of VLDB Workshops (VLDBW23) published by the CEUR Workshop Proceedings (CEUR-WS.org).

CEUR papers are indexed in Scopus, DBLP, SJR and other bibliographic databases. Altogether, this ensures maximum visibility to all who are interested in the topics of our workshop.

8. Stipend

Quantum Brilliance is a proud sponsor of the 2023 edition

of Quantum Data Science and Management. Quantum Brilliance develops room-temperature diamond quantum accelerators for massively parallelized, edge and ubiquitous quantum computing. Our industry sponsor Quantum Brilliance ofers to supplement the registration fees for young researchers, as well as researchers from underrepresented counties and communities.

9. Summary and Conclusions

Quantum computing hardware and software are at an

early stage. Although we do not currently have realworld applications showing a quantum advantage over

References

[1] M. Schönberger , I. Trummer, W. Mauerer, Quantum optimisation of general join trees , Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) - International Workshop on Quantum Data Science and Management (QDSM'23) ( 2023 ).

[2] M. Schönberger , S. Scherzinger , W. Mauerer, Ready to leap (by co-design)? join order optimisation on quantum hardware , Proc. ACM Manag. Data 1 ( 2023 ). URL: https://doi.org/10.1145/3588946. doi: 10 .1145/ 3588946.

[3] Y. Tjandra , H. S. Sugiarto , An evolutionary algorithm design for pauli-based quantum kernel classification , Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) - International Workshop on Quantum Data Science and Management (QDSM'23) ( 2023 ).

[4] V. Adoni , S. F. Hafshejani , D. Gaur , Empirical evaluation of a quantum accelerated approach for the central path method in linear programming , Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) - International Workshop on Quantum Data Science and Management (QDSM'23) ( 2023 ).

[5] L. Gruenwald , T. Winker , U. Çalıkyılmaz , J. Groppe , S. Groppe , Index tuning with machine learning on quantum computers for large-scale database applications , Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) - International Workshop on Quantum Data Science and Management (QDSM'23) ( 2023 ).

[6] G. Yuan , J. Lu , Y. Chen , S. Wu , C. Yao , Z. Yan , T. Li , G. Chen, Quantum computing for databases: A short survey and vision , Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW'23) - International Workshop on Quantum Data Science and Management (QDSM'23) ( 2023 ).