Speakers
Prof.
Nir Weinberger
(Technion-Israel Institute of Technology)
Sreejith Sreekumar
(L2S, CNRS, CentraleSupelec, University Paris-Saclay)
Description
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 (QMLP) 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 derive non-asymptotic performance guarantees for QMLP 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.
| Topic | Quantum algorithms for machine learning applications |
|---|---|
| Additional Topic | Theoretical foundations of quantum learning |
Authors
Prof.
Nir Weinberger
(Technion-Israel Institute of Technology)
Sreejith Sreekumar
(L2S, CNRS, CentraleSupelec, University Paris-Saclay)