Author

S. Sreekumar

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Preprint Oct 2026

Fourier Symmetrization for Geometric Quantum Machine Learning

Geometric quantum machine learning incorporates symmetry into quantum models, but how symmetry shapes their expressivity and guides effective model design remains insufficiently understood. We address this question through the Fourier representation of quantum Fourier models (QFMs). Symmetry organizes the frequency spe...

Le-Tao Wang, Abdel Lisser, S. Sreekumar et al. · 0 citations

Preprint Aug 2026

Information Bottleneck under Perfect Privacy

In this work, we study the information bottleneck under perfect privacy, with particular emphasis on the active-rate regime, where the representation-rate constraint is binding and directly limits the achievable utility. The goal is to construct a representation that preserves utility-relevant information while remaini...

Junle Zhong, Mohamad Assaad, S. Sreekumar · 0 citations

#machine learning Feb 2026

Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings

This approach provides a unified framework to handle MLP within both classical and quantum LLMs and shows that the Pythagorean inequality continues to hold in the infinite-dimensional setting whenever the convex information-projection problem attains a finite minimum.

S. Sreekumar, Nir Weinberger · arXiv.org · 0 citations