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  • Perspective
  • Open Access

Toward structure-preserving quantum encodings

Arthur J. Parzygnat1,2,*, Tai-Danae Bradley3,4,†, Andrew Vlasic5,‡, and Anh Pham6

  • *Contact author: arthurjp@mit.edu
  • †Contact author: tai.danae@math3ma.com
  • ‡Contact author: avlasic@deloitte.com
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Phys. Rev. Research 7, 041001 – Published 8 December, 2025

DOI: https://doi.org/10.1103/rph8-g15q

Abstract

Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings. The choice of such encodings may vary substantially from one task to another, and there exist only a few cases where structure has provided insight into their design and implementation, such as symmetry in geometric quantum learning. Here, we propose the perspective that category theory offers a natural mathematical framework for analyzing encodings that respect structure inherent in datasets and learning tasks. We illustrate this with pedagogical examples, which include geometric quantum machine learning, quantum metric learning, topological data analysis, and more. Moreover, such a perspective provides a language in which to ask meaningful and mathematically precise questions for the design of quantum encodings and circuits for quantum machine learning tasks.

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Phys. Rev. Research 7, 041001– Published 8 December, 2025
  • Received 11 April 2025
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