AI-Driven Quality Assurance in Cloud-Based Data Systems : Quantum Machine Learning for Accelerating Data Quality Metrics Calculation

Abstract

Ensuring data quality in cloud-based systems is a critical challenge, particularly as organizations scale to handle vast, complex, and dynamic datasets. Traditional AI-driven quality assurance techniques struggle with computational inefficiencies, latency, and scalability when applied to real-time, high-dimensional data validation. This research proposes a novel Quantum Machine Learning (QML)-enhanced data quality framework that significantly accelerates data quality metric calculations in cloud-native environments. By leveraging Quantum Kernel Methods (QKMs), Quantum Boltzmann Machines (QBMs), and Variational Quantum Circuits (VQCs), this study introduces a hybrid Quantum-Classical AI model for anomaly detection, consistency validation, and predictive data governance. The framework integrates seamlessly with existing cloud platforms such as AWS, GCP, and Azure, optimizing ETL pipelines and real-time data validation processes. Empirical validation, using benchmark datasets from finance, healthcare, and IoT, demonstrates that QML-based models achieve up to 10x speed-up in data consistency verification, anomaly detection, and real-time data quality assurance compared to classical AI/ML approaches. This study also provides theoretical insights into the computational speed-up advantage offered by quantum systems and explores the potential impact of Quantum Federated Learning (QFL) and Quantum Data Lakes (QDLs) for large-scale cloud governance. The findings contribute to the emerging field of Quantum AI for data-driven cloud computing, providing both academic and industry stakeholders with a roadmap for next-generation, high-speed, AI-enhanced data quality assurance methodologies.