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Quantum Machine Learning for Ultra-Fast Query Execution in High-Dimensional SQL Data Systems

Author(s) Raghavender Maddali
Country India
Abstract The new Quantum Machine Learning (QML) paradigm for highly efficient query execution in high-dimensional SQL data systems and Conventional database query execution is plagued by performance bottlenecks because of the explosive nature of structured data and intricate query optimization issues. The new QML-based methodology uses quantum algorithms to accelerate query processing by exploiting parallel computation, quantum-aided indexing, and probabilistic data access. With the incorporation of quantum-enhanced optimization methods, the framework achieves remarkable query execution time reduction, enhanced system scalability, and increased efficiency in managing relational databases at scale. The work compares the framework's performance against traditional SQL query optimizers and shows better performance in terms of execution speed, accuracy in retrieving data, and utilization of computational resources. The results also point to the promise of QML in revolutionizing DBMS and bringing in next-generation data analytics solutions.
Keywords Quantum Machine Learning, High-Dimensional SQL Data Systems, Ultra-Fast Query Execution, Quantum Query Optimization, Parallel Processing, Quantum-Assisted Indexing, Scalable Database Management, Quantum Algorithms for Databases, Probabilistic Data Retrieval, Next-Generation DBMS.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 3, Issue 4, April 2022
Published On 2022-04-09
Cite This Quantum Machine Learning for Ultra-Fast Query Execution in High-Dimensional SQL Data Systems - Raghavender Maddali - IJLRP Volume 3, Issue 4, April 2022. DOI 10.5281/zenodo.15107548
DOI https://doi.org/10.5281/zenodo.15107548
Short DOI https://doi.org/g8986n
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