Quantum Sampling Algorithms, Phase Transitions, and Computational Complexity

Drawing independent samples from a probability distribution is an important computational problem with applications in Monte Carlo algorithms, machine learning, and statistical physics. The problem can in principle be solved on a quantum computer by preparing a quantum state that encodes the entire...

Full description

Saved in:

Bibliographic Details
Published in:	arXiv.org
Main Authors:	Wild, Dominik S, Sels, Dries, Pichler, Hannes, Zanoci, Cristian, Lukin, Mikhail D
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 07.09.2021
Subjects:	Adiabatic flow Algorithms Complexity Graphs Hamiltonian functions Ising model Machine learning Markov chains Neutral atoms Phase transitions Probability distribution Quantum computers Rydberg states Sampling Statistical analysis Statistical methods
ISSN:	2331-8422
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!