Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings

Abstract:Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models. Here, we study a quantum version of this task for a simplified data model consisting of independent and identically distributed samples, as a first step. The quantum maximum likelihood predictor is obtained by embedding of empirical probability distributions into quantum states and performing a minimization of quantum relative entropy over a given class of states. We provide an interpretation of this predictor in terms of quantum reverse information projection and quantum Pythagorean theorem when the class of quantum models is sufficiently expressive. We further derive non-asymptotic performance guarantees in terms of convergence rates and concentration inequalities, both in trace norm and quantum relative entropy. Our approach provides a unified framework to handle MLP within both classical and quantum LLMs.

Information Theory (cs.IT); Machine Learning (cs.LG); Quantum Physics (quant-ph); Machine Learning (stat.ML)

arXiv:2602.18364 [cs.IT]

(or arXiv:2602.18364v2 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2602.18364 Focus to learn more arXiv-issued DOI via DataCite

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