Quantum annealing is a meta-heuristic approach tailored to solve combinatorial optimization problems with quantum annealers. In this tutorial, we provide a fundamental and comprehensive introduction to quantum annealing and modern data management systems and show quantum annealing's potential benefits and applications in the realm of database optimization. We demonstrate how to apply quantum annealing for selected database optimization problems, which are critical challenges in many data management platforms. The demonstrations include solving join order optimization problems in relational databases, optimizing sophisticated transaction scheduling, and allocating virtual machines within cloud-based architectures with respect to sustainability metrics. On the one hand, the demonstrations show how to apply quantum annealing on key problems of database management systems (join order selection, transaction scheduling), and on the other hand, they show how quantum annealing can be integrated as a part of larger and dynamic optimization pipelines (virtual machine allocation). The goal of our tutorial is to provide a centralized and condensed source regarding theories and applications of quantum annealing technology for database researchers, practitioners, and everyone who wants to understand how to potentially optimize data management with quantum computing in practice. Besides, we identify the advantages, limitations, and potentials of quantum computing for future database and data management research.
Quantum annealing is a meta-heuristic approach tailored to solve combinatorial optimization problems with quantum annealers. In this tutorial, we provide a fundamental and comprehensive introduction to quantum annealing and modern data management systems and show quantum annealing's potential benefits and applications in the realm of database optimization. We demonstrate how to apply quantum annealing for selected database optimization problems, which are critical challenges in many data management platforms. The demonstrations include solving join order optimization problems in relational databases, optimizing sophisticated transaction scheduling, and allocating virtual machines within cloud-based architectures with respect to sustainability metrics. On the one hand, the demonstrations show how to apply quantum annealing on key problems of database management systems (join order selection, transaction scheduling), and on the other hand, they show how quantum annealing can be integrated as a part of larger and dynamic optimization pipelines (virtual machine allocation). The goal of our tutorial is to provide a centralized and condensed source regarding theories and applications of quantum annealing technology for database researchers, practitioners, and everyone who wants to understand how to potentially optimize data management with quantum computing in practice. Besides, we identify the advantages, limitations, and potentials of quantum computing for future database and data management research.
Quantum annealing has emerged as a transformative approach in solving combinatorial optimization problems by leveraging the principles of quantum mechanics. A comprehensive survey of quantum annealing is presented, delineating its foundational concepts, state-of-the-art advancements, and diverse applications across domains such as logistics, finance, machine learning, and energy optimization. Notable contributions include the introduction of a systematic benchmarking framework to evaluate quantum annealing against classical optimization techniques across metrics like time-to-target, scalability, and energy efficiency. Additionally, novel hybrid quantum-classical paradigms and hardware advancements are explored, addressing challenges in qubit connectivity, coherence, and noise resilience. Optimization problems are mapped to suitable quantum annealing platforms, offering practical guidelines for leveraging quantum technologies in real-world scenarios. Critical gaps in scalability and integration with machine learning are identified, with future research directions proposed to bridge these challenges. These findings provide a holistic understanding of quantum annealing's potential, offering a foundational resource for advancing quantum computing in optimization.
Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the QA algorithm for programmable use. Specifically, QA processors produced by D-Wave systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of QA technology. We identify the advantages, limitations, and potential of QA for both researchers and practitioners from various fields.
Combinatorial optimization is a foundation of numerous scientific, industrial, and social decision-making questions, but its usefulness is mostly restricted by the exponential complexity of the problem of classical computability. The quantum annealing (QA) has become one of the leading analogue quantum systems to address such problems, whereby optimization problems are converted into physical energy structures and quantum fluctuations are utilized to perform systematic exploration. It provides a critical, synthesized, and comprehensive review of quantum annealing as applied to combinatorial optimization, including the theoretical basis, hardware designs, algorithm methods, issues of embedding and encoding, benchmarking procedures, applications, and integration with quantum algorithms based on gates and solvers on a classical computer. We build a comprehensive taxonomy between adiabatic dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to state-of-the-art flux-qubit annealers, new architectures, and hybrid quantum classical pipelines. We show that the overhead of embedding and encoding and the benchmarking approach taken are the most significant factors in determining scalability and performance, but not the raw number of qubits. Transportation, energy systems, robotics, finance, pharmacological discovery, and machine learning are just some examples of the domain-specific case studies that use QA with minimal empirical utility as a hybrid refinement engine and not an independent solver. Moreover, we introduce a stringent evaluation of the existing benchmarking culture, clarify the causal links between QA and QAOA as well as between VQE and structural predicaments, and reveal the institutional roots of the existing restrictions. We conclude by identifying a research roadmap in the future, focusing on the annealing hardness characterization, Stoquastic control manipulation, embedding rules automation, architecture design, and development of principled quantum advantage. It creates a benchmark reference to those in the field and research and deployment of scalable, reliable, and application-relevant quantum optimization
The application of quantum computing to data management has attracted growing interest, yet remains constrained by a limited understanding of how the physical behaviour of quantum devices relates to the structure and difficulty of database problems. In particular, evaluating quantum annealing approaches for combinatorial optimisation, which is central to many data management tasks, poses significant challenges beyond the scope of conventional empirical and complexity-theoretic methods.
We present a computational toolbox for the systematic numerical analysis of quantum annealing processes derived from data management problem formulations. Adopting a physics-informed perspective, the toolbox enables the study of spectral and dynamical properties -- such as energy gaps and eigenstate structure -- that are inaccessible through direct hardware measurements, yet essential for understanding computational hardness and scaling behaviour.
Our approach further provides derived quantities and visualisation techniques that support the interpretation of optimisation dynamics, the identification of structural similarities to canonical physical models, and the construction of reduced effective descriptions. By bridging methodological gaps between quantum computing and database systems research, this work establishes a principled foundation for evaluating quantum approaches and guiding future co-design efforts.
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.