The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.
The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.
This article explores the transformative potential of quantum computing in database systems, focusing on advanced query operations that traditionally challenge conventional architectures. It examines how quantum mechanical phenomena like superposition and entanglement enable unprecedented parallel processing capabilities in database operations. The article investigates key areas including pattern matching, similarity searches, and graph traversal algorithms, where quantum advantages show particular promise. It compares quantum and classical systems, addressing scalability considerations and accuracy challenges in quantum database implementations. The article delves into hybrid quantum-classical architectures and quantum native approaches, examining their respective strengths and limitations. Special attention is given to the challenges and opportunities presented by Noisy Intermediate-Scale Quantum (NISQ) devices, including hardware limitations, algorithm development, and practical integration considerations. The article provides insights into the future directions of quantum databases, emphasizing the importance of developing robust error correction mechanisms and optimizing quantum circuits for practical database operations.
Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using quantum computing techniques. This tutorial aims to establish the theoretical foundation essential for enhancing methodologies and practical implementations in this line of research. Moreover, this tutorial takes a forward-looking approach by delving into recent strides in quantum internet technologies and the nonlocality theory. We aim to shed light on the uncharted territory of future data systems tailored for the quantum internet.
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.
Quantum computing has evolved from a largely theoretical discipline into a field of growing interest for commercial and industrial applications. Recent advances in quantum hardware, algorithms, and cloud-based access have enabled early experimentation outside academic environments, raising questions about its potential impact on real-world decision-making and operational problems. This paper provides an overview of the current state of quantum computing with a focus on its emerging relevance in commercial contexts. It examines the categories of problems that have attracted attention from industry, particularly those involving high combinatorial complexity such as optimization, scheduling, logistics, and resource allocation. The analysis also reviews the global landscape of companies and initiatives engaged in quantum computing, highlighting different technological approaches, maturity levels, and regional characteristics. In parallel, the paper discusses the principal challenges and limitations that constrain widespread adoption, including hardware immaturity, algorithmic readiness, cost factors, and organizational requirements. By considering both opportunities and risks, this study aims to present a balanced and realistic perspective on the role quantum computing may play in business environments. The objective is to clarify the current position of quantum computing within the broader technological landscape and to support informed interpretation of its potential, limitations, and future trajectory in commercial applications.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.