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...

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Vydané v:	arXiv.org
Hlavní autori:	Wild, Dominik S, Sels, Dries, Pichler, Hannes, Zanoci, Cristian, Lukin, Mikhail D
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 07.09.2021
Predmet:	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
On-line prístup:	Získať plný text
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