Tutorials

Utilizing Quantum Computing to Improve the Quality of Data

Abstract

In today's data-driven world, ensuring data quality has become the key to success for organizations across industries and academia. Hence, this tutorial begins by exploring the foundational principles of data quality, emphasizing dimensions such as accuracy, completeness, consistency, timeliness, and validity. We will dive into strategies for identifying and addressing common data quality issues, including data duplication, missing values, and errors in data entry or processing. However, many strategies for improving data quality are costly in terms of processing time, computational resources, and/or the needed amount of training data. Furthermore, applied heuristic methods return suboptimal results. Current quantum computing research explores whether these types of computational challenges could benefit from quantum computers. Quantum computing is rapidly emerging as a transformative technology, marked by recent breakthroughs and a potentially revolutionary computational paradigm. Although it is still in its early stages, improved quantum hardware, growing global awareness, and investments underscore its potential to transform various industries. This tutorial will briefly introduce quantum computing and focus on its applications to data quality, including quantum machine learning. We will discuss the potential of quantum computing and show use cases utilizing quantum machine learning and quantum optimization for data quality issues. We will also point out open challenges that might open new research directions and new business opportunities in improving data quality.

Presenters

Valter Uotila is a Ph.D. student at the University of Helsinki, specializing in quantum computing applications for databases and data management. He has previously presented tutorials on these topics at SIGMOD 2023 and IEEE Quantum Week 2024. He has been a proceedings chair for the Quantum Data Science and Management Workshops co-located with VLDB. Beyond his primary research, Valter is also interested in distributed quantum computing, quantum information theory, and applied category theory, as well as their synergies with data management systems. He has secured top-three placements in several international hackathons, such as QHack 2022 and 2023, the Quantum Internet Application Challenge by QIA, and BMW's Quantum Computing for Automotive Challenges.

Soror Sahri is an Associate Professor at Université Paris Cité. Her research interests focus on data management, including big data analytics, data quality, and query processing. She has been involved in various international and interdisciplinary projects, and is the project coordinator of the ANR-DFG QualityOnt project "High-Quality Knowledge Graphs from Recent English, French, and German Emergent Trends with the Example of COVID-19". She is a member of the editorial board of TLDKS journal, and has served as a local organization chair of IEEE-MASCOTS 2014, and general co-chair of KGSWC 2024. She served as PC member and session chair of many national and international conferences.

Sven Groppe is a Professor at the University of Lübeck and the project coordinator of the BMBF-funded project QC4DB – Accelerating Relational Database Management Systems via Quantum Computing. Furthermore, he is principal investigator in the ANR-DFG funded project "High Quality Knowledge Graphs from recent English, French and German Emergent Trends with the example of COVID-19" (QualityOnt). Previous projects cover topics about Semantic Web databases, GPU and FPGA hardware acceleration of relational and Semantic Web databases, and advanced data management techniques for the Semantic Internet-of-Things. He is a full member of the International Federation for Information Processing (IFIP) Working Group WG2.6 Database. He has been a member of the RDF Data Access Working Group, which has been a working group of the World Wide Web Consortium (W3C) to specify SPARQL, and the Rule Interchange Format Working Group of the W3C. Over 125 program committee memberships in international conferences and workshops, reviewing activities in over 40 internationally recognized journals and for 8 funding organizations, editorial activities in 4 journals and chair of Quantum Data Science and Management, Semantic Big Data, Big Data in Emergent Distributed Environments, and Very Large Internet of Things - Workshops at the first-class ACM SIGMOD and VLDB conferences as well as general chair of the International Semantic Intelligence Conference, International Conference on Applied Machine Learning and Data Analytics, International Health Informatics Conference (IHIC) and other conferences as well as co-authorship with over 190 scientists from 28 countries on 6 continents are hints for a strong integration into the scientific community. For more details about his academic career, visit https://www.ifis.uni-luebeck.de/~groppe