解决量子计算机上数据库模式匹配的硬变体问题

IF 2.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611603

Kristin Fritsch, Stefanie Scherzinger

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引用次数: 0

摘要

随着量子计算机现在可以作为云服务使用，全球都在寻求能够显示量子优势的应用。自然，数据管理是一个候选领域。可行的解决方案需要设计混合量子算法，其中量子计算单元(QPU)和经典计算(通过cpu)合作解决问题。这个演示演示了针对数据库模式匹配的NP-hard变体的端到端解决方案。我们的演示旨在具有教育意义(并希望具有启发性)，允许参与者探索关键的设计决策，例如基于QPU和基于cpu的计算阶段之间的切换。它还将允许参与者亲身体验-通过有趣的互动-如何轻松地超越当今qpu的限制问题的大小。

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Solving Hard Variants of Database Schema Matching on Quantum Computers

With quantum computers now available as cloud services, there is a global quest for applications where a quantum advantage can be shown. Naturally, data management is a candidate domain. Workable solutions require the design of hybrid quantum algorithms, where a quantum computing unit (a QPU) and classical computing (via CPUs) cooperate towards solving a problem. This demo illustrates such an end-to-end solution targeting NP-hard variants of database schema matching. Our demo is intended to be educational (and hopefully inspiring), allowing participants to explore the critical design decisions, such as the handover between phases of QPU- and CPU-based computation. It will also allow participants to experience hands-on - through playful interaction - how easily problem sizes exceed the limitations of today's QPUs.

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来源期刊

Proceedings of the Vldb Endowment Computer Science-General Computer Science

CiteScore

7.70

自引率

0.00%

发文量

期刊介绍： The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.