SQL2Circuits: Estimating Cardinalities, Execution Times, and Costs for SQL Queries with Quantum Natural Language Processing
Valter Uotila

TL;DR
This paper introduces a quantum machine learning model that estimates SQL query metrics like cardinalities and costs, leveraging quantum natural language processing techniques for improved database query optimization.
Contribution
It adapts quantum natural language processing encoding mechanisms to database query optimization, providing a scalable, explainable, and accurate quantum approach for estimating query metrics.
Findings
Achieves high accuracy in estimating query metrics.
Demonstrates scalability with hundreds of queries.
Provides theoretical analysis of model expressibility and entangling capabilities.
Abstract
Recent advances in quantum computing have led to progress in exploring quantum applications across diverse fields, including databases and data management. This work presents a quantum machine learning model that tackles the challenge of estimating metrics, such as cardinalities, execution times, and costs, for SQL queries in relational databases. Precise estimations are crucial for the query optimizer to optimize query processing in relational databases efficiently. Our proposed quantum machine learning model consists of a novel query encoding mechanism, which maps SQL queries into high-dimensional Hilbert spaces using grammatical representations of the queries. The encoding mechanism translates SQL queries into parameterized quantum circuits, forming the core of the quantum machine learning model. The parameters in this model are tuned using standard quantum machine learning…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
