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To evaluate the potential of Quantum Computing (QC) on real data management tasks in this paper we elaborate on the task of encoding large in size Knowledge Graphs as numerical data for using them for creating the corresponding quantum state. This is a task that cannot be bypassed, and has to be performed using a classical computer. We report experimental results for datasets up to size 800 million triples, in particular for the entire DBpedia. We also report times for creating the corresponding quantum states by superimposing the encoded datasets. Finally, since a significant number of tasks are reduced to Grover’s algorithm, we report efficiency results on running this algorithm on datasets with size up to \(10^8\) bitstrings on a simulator and on IBM Quantum Computer. Even if the output of Grover is not stable on current quantum hardware, this analysis is useful for estimating the efficiency and thus identifying to what tasks the application of QC will be beneficial.

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On Encoding Big Knowledge Graphs as Quantum States and on Running Grover’s Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Giorgos Theodorakis,
  • Michalis Touloupakis,
  • Yannis Tzitzikas

摘要

To evaluate the potential of Quantum Computing (QC) on real data management tasks in this paper we elaborate on the task of encoding large in size Knowledge Graphs as numerical data for using them for creating the corresponding quantum state. This is a task that cannot be bypassed, and has to be performed using a classical computer. We report experimental results for datasets up to size 800 million triples, in particular for the entire DBpedia. We also report times for creating the corresponding quantum states by superimposing the encoded datasets. Finally, since a significant number of tasks are reduced to Grover’s algorithm, we report efficiency results on running this algorithm on datasets with size up to \(10^8\) bitstrings on a simulator and on IBM Quantum Computer. Even if the output of Grover is not stable on current quantum hardware, this analysis is useful for estimating the efficiency and thus identifying to what tasks the application of QC will be beneficial.