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Abstract <p>An approach to the development of hybrid quantum-classical data analysis pipelines based on the composition of ready-made quantum templates of the PennyLane framework is proposed. A hybrid quantum-classical machine learning pipeline for classification tasks has been developed and successfully tested, demonstrating high accuracy on a number of test datasets and the ability to scale the architecture.</p>

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Development of Hybrid Data Analysis Pipelines Using the PennyLane Framework

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • P. V. Zrelov,
  • O. V. Ivantsova,
  • M. S. Katulin

摘要

Abstract

An approach to the development of hybrid quantum-classical data analysis pipelines based on the composition of ready-made quantum templates of the PennyLane framework is proposed. A hybrid quantum-classical machine learning pipeline for classification tasks has been developed and successfully tested, demonstrating high accuracy on a number of test datasets and the ability to scale the architecture.