QCE'24 Tutorial: Quantum Annealing -- Emerging Exploration for Database Optimization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929581697531904 |
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| author | Nayak, Nitin Schönberger, Manuel Uotila, Valter Yan, Zhengtong Groppe, Sven Lu, Jiaheng Mauerer, Wolfgang |
| author_facet | Nayak, Nitin Schönberger, Manuel Uotila, Valter Yan, Zhengtong Groppe, Sven Lu, Jiaheng Mauerer, Wolfgang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QCE'24 Tutorial: Quantum Annealing -- Emerging Exploration for Database Optimization Nayak, Nitin Schönberger, Manuel Uotila, Valter Yan, Zhengtong Groppe, Sven Lu, Jiaheng Mauerer, Wolfgang Quantum Physics Databases 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. |
| title | QCE'24 Tutorial: Quantum Annealing -- Emerging Exploration for Database Optimization |
| topic | Quantum Physics Databases |
| url | https://arxiv.org/abs/2411.04638 |