Work

Opportunities and Challenges for Data Quality in the Era of Quantum Computing

Year: 2026

Type: conference-paper

Abstract: In an era in which data underpin decision-making across science, politics, and economics, ensuring high data quality is paramount. Conventional computing algorithms for enhancing data quality, including anomaly detection, demand substantial computational resources, lengthy processing times, and extensive training datasets. This work surveys the potential advantages of quantum computing for improving data quality, with a particular focus on anomaly detection. We begin by examining quantum techniques that could replace key subroutines in conventional anomaly-detection frameworks to mitigate their computational cost. This survey will especially serve as a guide to see the connection between classical and quantum methods in the data quality domain. We identify unresolved challenges and limitations in applying quantum computing to data quality tasks. Our findings open new avenues for research and applications that aim to enhance data-quality solutions using quantum technologies.

Source: Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data)

DOI: 10.1145/3811628.3811834

Authors: Sven Groppe, Valter Uotila, Jinghua Groppe

Institutions: TU Bergakademie Freiberg, Aalto University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4416955732

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