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Quantum Machine Learning

Information-Geometric Optimization on Spheres

arXiv

Authors: Vladimir Ja\' cimovi\'c

Year

2026

Paper ID

68570

Status

Preprint

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~2 min

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Abstract

We consider the black-box optimization problem on a sphere. Two information-geometric optimization flows (IGO flows) are designed with rigorous calculation of natural search gradients based on hyperbolic (information) geometry of Poincar' e and Bergman balls. We demonstrate that ensembles of generalized Kuramoto oscillators on spheres compute natural search gradients and realize IGO algorithms on both manifolds. The relationship between natural gradient policies in Bergman balls and quantum decision making is pointed out.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We consider the black-box optimization problem on a sphere.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2606.07588 [2] arXiv https://arxiv.org/abs/2606.07588 [3] Publisher https://arxiv.org/abs/2606.07588

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External citation index: OpenAlex citation signal • updated 2026-06-14 08:23:34

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