Quantum Geometry of Data

• URL: http://arxiv.org/abs/2507.21135v1
• Date: Tue, 22 Jul 2025 21:27:22 GMT
• Title: Quantum Geometry of Data
• Authors: Alexander G. Abanov, Luca Candelori, Harold C. Steinacker, Martin T. Wells, Jerome R. Busemeyer, Cameron J. Hogan, Vahagn Kirakosyan, Nicola Marzari, Sunil Pinnamaneni, Dario Villani, Mengjia Xu, Kharen Musaelian,
• Abstract summary: We show how Quantum Cognition Machine Learning encodes data as quantum geometry.<n>In QCML, features of the data are represented by learned Hermitian matrices, and data points are mapped to states in Hilbert space.
• Score: 30.66112400189209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We demonstrate how Quantum Cognition Machine Learning (QCML) encodes data as quantum geometry. In QCML, features of the data are represented by learned Hermitian matrices, and data points are mapped to states in Hilbert space. The quantum geometry description endows the dataset with rich geometric and topological structure - including intrinsic dimension, quantum metric, and Berry curvature - derived directly from the data. QCML captures global properties of data, while avoiding the curse of dimensionality inherent in local methods. We illustrate this on a number of synthetic and real-world examples. Quantum geometric representation of QCML could advance our understanding of cognitive phenomena within the framework of quantum cognition.

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