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International Journal of Quantum Information

CNOT-based design and query management in quantum relational databases

Amor Gueddana, Rihab Chatta, Moez Attia

Published Jun 1, 2014• 7 citations
Publisher

Abstract

In this work, we propose a complete design of a quantum relational multi-tables database. We illustrate how to perform basic and advanced queries to insert, update, delete and select records from single or joined digitized tables. A suggestion of a Quantum Query Language (QQL) is then addressed and we illustrate for a simple quantum database how QQL performs. We highlight the used scheme allowing to traduce the QQL semantics to the corresponding CNOT-based implementation and we underline the evolution of the amplitude of probability of the superposed states contained in the tables.

DOI
10.1142/s0219749914500233

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In this work, we propose a complete design of a quantum relational multi-tables database. We illustrate how to perform basic and advanced queries to insert, update, delete and select records from single or joined digitized tables. A suggestion of a Quantum Query Language (QQL) is then addressed and we illustrate for a simple quantum database how QQL performs. We highlight the used scheme allowing to traduce the QQL semantics to the corresponding CNOT-based implementation and we underline the evolution of the amplitude of probability of the superposed states contained in the tables.
September 2006Abstract: Traditional database query languages are based on set theory and crisp first order logic. However, many applications require retrieval-like queries which return result objects associated with a degree value of being relevant to the query. Traditionally, retrieval systems estimate relevance by exploiting hidden object semantics whereas query processing in database systems relies on matching select-conditions with attribute values. Thus, different mechanisms were developed for database and information retrieval systems. In consequence, there is a lack of support for queries involving both retrieval and database search terms. In this work, we develop a unifying framework based on the mathematical formalism of quantum mechanics and quantum logic. Van Rijsbergen already discussed the strong relation between the formalism of quantum mechanics and information retrieval. The goal of this work is to interrelate concepts from database query processing to concepts from quantum mechanics and logic. As result, we obtain a common theory which allows us to incorporate seamlessly retrieval search into traditional database query processing. Exploiting our theoretical results, we introduce the quantum query language QQL. In contrast to competing approaches, our formalism is based on quantum logic.
Recent advances in quantum computing have led to early-stage platforms with over 1,000 qubits, alongside roadmaps targeting fault-tolerant, industry-scale systems within this decade. To harness the immense potential of these emerging systems, it is essential to explore the feasibility of hosting relational database engines on quantum platforms. Encouragingly, several early studies have already investigated the applicability of quantum computing to core DBMS components such as multi-query optimization, join-order optimization and index-configuration selection offering initial insights into key challenges and practical implementation considerations.
This paper introduces a novel approach to relational database management systems (RDBMS) leveraging quantum-inspired techniques, specifically tensor network representations, to enhance relational reasoning in distributed environments. Traditional RDBMS rely on symbolic representations of relationships, which often struggle to scale effectively when dealing with complex, interconnected datasets. The proposed methodology addresses this limitation by employing tensor networks to model and reason about relationships. Nodes within the tensor network represent individual data points, and tensor contractions simulate relational queries, offering the potential for exponential speedups in operations like joins and aggregations – tasks that are computationally intensive in conventional RDBMS. The core innovation lies in applying quantum-inspired concepts to relational reasoning, providing a fundamentally different paradigm for data management and promising improved performance and scalability. We explore the theoretical foundations of this approach and outline a framework for its implementation, focusing on the challenges and opportunities presented by distributed database architectures.
With the development of quantum computing, significant advancements have been made since the 1980s, leading to the birth of quantum computers and sparking widespread enthusiasm in this emerging field. While quantum computing is still in its early stages of development, researchers are increasingly drawn to its magic power and have identified numerous compelling applications across various domains, ranging from cryptography, query optimization, and machine learning. Notably, one of the most promising applications lies within the realm of database management systems, giving rise to quantum computing for databases, which holds the potential to offer substantial advantages over classical databases, particularly in terms of query processing speed and efficiency, as well as saving memory space. This vision paper aims to provide an overview of the current research challenges and opportunities of quantum computing for the field of databases, as well as outline the potential for the development of a quantum multi-modal database.