Improved algorithms for learning quantum Hamiltonians, via flat polynomials

Fuente: arXiv
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Autor principal: Narayanan, Shyam
Formato: Preprint
Publicado: 2024
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author Narayanan, Shyam
author_facet Narayanan, Shyam
contents We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample complexity and runtime dependence to singly exponential in the inverse-temperature parameter, as opposed to doubly exponential. Our main technical contribution is a new flat polynomial approximation to the exponential function, with significantly lower degree than the flat polynomial approximation used in [BLMT24].
format Preprint
id arxiv_https___arxiv_org_abs_2407_04540
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Improved algorithms for learning quantum Hamiltonians, via flat polynomials
Narayanan, Shyam
Quantum Physics
Data Structures and Algorithms
Machine Learning
We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample complexity and runtime dependence to singly exponential in the inverse-temperature parameter, as opposed to doubly exponential. Our main technical contribution is a new flat polynomial approximation to the exponential function, with significantly lower degree than the flat polynomial approximation used in [BLMT24].
title Improved algorithms for learning quantum Hamiltonians, via flat polynomials
topic Quantum Physics
Data Structures and Algorithms
Machine Learning
url https://arxiv.org/abs/2407.04540