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In this paper, we compare different approaches for prototype classification learning schemes on spherical manifolds and consider them from the perspective of quantum computing. More precisely, these approaches are based on the concept of learning vector quantization, i.e. prototype based models which are known for its robustness and interpretability. We analyze them to what extent these models are applicable on quantum devices and which quantum assumptions are met. Thereby, the relation of learning on spherical manifolds to the learning on Bloch-spheres in quantum approaches is of special interest.

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Prototype Learning for Classification on Spherical Manifolds and Its Relation to Quantum Classification Approaches

  • 会议论文

  • 发表日期：

    2026年

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  • Alexander Engelsberger,
  • M. Psenickova,
  • Thomas Villmann

摘要

In this paper, we compare different approaches for prototype classification learning schemes on spherical manifolds and consider them from the perspective of quantum computing. More precisely, these approaches are based on the concept of learning vector quantization, i.e. prototype based models which are known for its robustness and interpretability. We analyze them to what extent these models are applicable on quantum devices and which quantum assumptions are met. Thereby, the relation of learning on spherical manifolds to the learning on Bloch-spheres in quantum approaches is of special interest.