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Quantum Data Sketches

Authors Qin Zhang , Mohsen Heidari

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Author Details

Qin Zhang
  • Indiana University Bloomington, IN, USA
Mohsen Heidari
  • Indiana University Bloomington, IN, USA

Funding

  • Zhang, Qin: supported by NSF CCF-1844234 and IU Luddy Faculty Fellowship.
  • Heidari, Mohsen: supported by NSF CCF-2211423.

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Qin Zhang and Mohsen Heidari. Quantum Data Sketches. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 16:1-16:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.16

Abstract

Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress underscores the necessity for developing efficient and scalable quantum data management strategies. This goal faces immense challenges due to the exponential dimensionality of quantum data and its unique quantum properties such as no-cloning and measurement stochasticity. Specifically, classical storage and manipulation of an arbitrary n-qubit quantum state requires exponential space and time. Hence, there is a critical need to revisit foundational data management concepts and algorithms for quantum data. In this paper, we propose succinct quantum data sketches to support basic database operations such as search and selection. We view our work as an initial step towards the development of quantum data management model, opening up many possibilities for future research in this direction.

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum query complexity
  • Information systems → Query operators
  • Information systems → Query optimization
Keywords
  • quantum data representation
  • data sketching
  • query execution

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