Quantum Computing for Large-Scale Data Management

• Applicant: Dr. Manuel Schönberger
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 575573918

Prompted by the challenge of addressing ever-increasing computational loads, data management research has begun to assess novel hardware types and processing paradigms. Among these, quantum computing has been identified as a particularly promising paradigm, given its ability to exploit quantum-mechanical phenomena to obtain substantial speedups over conventional systems. Yet, contemporary quantum systems remain prototypical in nature, and are accordingly governed by various limitations. Their limited qubit capacity in particular hampers their scalability aptness, which has constrained their use for data management to small-scale problems. In this project, we thus seek to derive methods to enable the use of quantum systems for large-scale data management, to render them capable of processing industrially relevant data loads. By relying on (1) effective problem partitioning techniques, (2) methods to prune and effectively explore large solution spaces, as well as (3) adaptive and hybrid optimisation methods, we seek to overcome the capacity limitations that are inherent to contemporary quantum systems. Further, we seek to mitigate the limitations of current quantum devices by relying on hardware-software co-design methods, to optimally align algorithms and quantum hardware. We can thereby derive highly efficient algorithms for large-scale quantum data management.

• DFG Programme: WBP Fellowship
• International Connection: USA
• Host: Professor Dr. Immanuel Trummer