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Abstract <p>The authors investigate a quantum support vector algorithm that uses qudits to identify the most accurate way of solving a prototype machine learning task: the binary classification of point clusters. Different ways of classifying points are examined and the ones that are most efficient are highlighted. The potential advantages of using quantum algorithms with qudits is discussed.</p>

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A Quantum Support Vector Machine with Multilevel Systems

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • E. V. Glazkova,
  • E. O. Kiktenko,
  • A. K. Fedorov

摘要

Abstract

The authors investigate a quantum support vector algorithm that uses qudits to identify the most accurate way of solving a prototype machine learning task: the binary classification of point clusters. Different ways of classifying points are examined and the ones that are most efficient are highlighted. The potential advantages of using quantum algorithms with qudits is discussed.