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

Sven Groppe, Valter Uotila, Jinghua Groppe

TL;DR

This paper investigates how quantum computing can address data quality challenges, focusing on anomaly detection. It develops two taxonomies mapping quantum subroutines to classical anomaly detection tasks and surveys current quantum anomaly detection work. It experimentally demonstrates quantum reservoir computing for volatility regime change detection in stock data, showing competitive performance with classical approaches. It discusses open challenges—hardware noise, data loading, and simulation limits—and outlines future research directions and potential commercial implications of quantum-enhanced data quality solutions.

Abstract

In an era where data underpins decision-making across science, politics, and economics, ensuring high data quality is of paramount importance. Conventional computing algorithms for enhancing data quality, including anomaly detection, demand substantial computational resources, lengthy processing times, and extensive training datasets. This work aims to explore the potential advantages of quantum computing for enhancing data quality, with a particular focus on detection. We begin by examining quantum techniques that could replace key subroutines in conventional anomaly detection frameworks to mitigate their computational intensity. We then provide practical demonstrations of quantum-based anomaly detection methods, highlighting their capabilities. We present a technical implementation for detecting volatility regime changes in stock market data using quantum reservoir computing, which is a special type of quantum machine learning model. The experimental results indicate that quantum-based embeddings are a competitive alternative to classical ones in this particular example. Finally, we identify unresolved challenges and limitations in applying quantum computing to data quality tasks. Our findings open up new avenues for innovative research and commercial applications that aim to advance data quality through quantum technologies.

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

TL;DR

This paper investigates how quantum computing can address data quality challenges, focusing on anomaly detection. It develops two taxonomies mapping quantum subroutines to classical anomaly detection tasks and surveys current quantum anomaly detection work. It experimentally demonstrates quantum reservoir computing for volatility regime change detection in stock data, showing competitive performance with classical approaches. It discusses open challenges—hardware noise, data loading, and simulation limits—and outlines future research directions and potential commercial implications of quantum-enhanced data quality solutions.

Abstract

In an era where data underpins decision-making across science, politics, and economics, ensuring high data quality is of paramount importance. Conventional computing algorithms for enhancing data quality, including anomaly detection, demand substantial computational resources, lengthy processing times, and extensive training datasets. This work aims to explore the potential advantages of quantum computing for enhancing data quality, with a particular focus on detection. We begin by examining quantum techniques that could replace key subroutines in conventional anomaly detection frameworks to mitigate their computational intensity. We then provide practical demonstrations of quantum-based anomaly detection methods, highlighting their capabilities. We present a technical implementation for detecting volatility regime changes in stock market data using quantum reservoir computing, which is a special type of quantum machine learning model. The experimental results indicate that quantum-based embeddings are a competitive alternative to classical ones in this particular example. Finally, we identify unresolved challenges and limitations in applying quantum computing to data quality tasks. Our findings open up new avenues for innovative research and commercial applications that aim to advance data quality through quantum technologies.

Paper Structure

This paper contains 14 sections, 8 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Potential of Quantum Computing for Data Quality
  3. Potential of Quantum Machine Learning
  4. Quantum Computer Wall
  5. Simulation Wall
  6. Data (and Use Case) Wall
  7. Potential of quantum computing in Anomaly Detection
  8. Experimental study on quantum reservoir computing
  9. Background on quantum reservoir computing
  10. Volatility regime change detection
  11. Quantum reservoir learning model
  12. Results
  13. Open Challenges and Future Research Directions
  14. Summary and Conclusions

Figures (3)

  • Figure 1: Taxonomy of Quantum Computing algorithms that can serve as drop-in replacements of classical subroutines in the referenced approaches to anomaly detection.
  • Figure 2: Taxonomy of the Quantum Computing algorithms, which have been used in quantum anomaly detection approaches
  • Figure 3: Timeline of the development of quantum computers across various vendors