Efficient Algorithms for Approximating Quantum Partition Functions at Low Temperature

01/17/2022
by   Tyler Helmuth, et al.
0

We establish an efficient approximation algorithm for the partition functions of a class of quantum spin systems at low temperature, which can be viewed as stable quantum perturbations of classical spin systems. Our algorithm is based on combining the contour representation of quantum spin systems of this type due to Borgs, Kotecký, and Ueltschi with the algorithmic framework developed by Helmuth, Perkins, and Regts, and Borgs et al.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/24/2020

Efficient Algorithms for Approximating Quantum Partition Functions

We establish a polynomial-time approximation algorithm for partition fun...
research
06/15/2023

Algorithmic Cluster Expansions for Quantum Problems

We establish a general framework for developing approximation algorithms...
research
09/20/2019

Efficient sampling and counting algorithms for the Potts model on Z^d at all temperatures

For d > 2 and all q≥ q_0(d) we give an efficient algorithm to approximat...
research
06/29/2018

Algorithmic Pirogov-Sinai theory

We develop an efficient algorithmic approach for approximate counting an...
research
07/18/2022

A Sublinear-Time Quantum Algorithm for Approximating Partition Functions

We present a novel quantum algorithm for estimating Gibbs partition func...
research
09/20/2017

Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics

We recently introduced a method to approximate functions of Hermitian Ma...
research
01/08/2021

Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data

Quantifying how far the output of a learning algorithm is from its targe...

Please sign up or login with your details

Forgot password? Click here to reset