Quantum Sampling Algorithms for Near-Term Devices
Abstract
Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning. We introduce a family of quantum algorithms that provide unbiased samples by preparing a state encoding the entire Gibbs distribution. We show that this approach leads to a speedup over a classical Markov chain algorithm for several examples including the Ising model and sampling from weighted independent sets of two different graphs. Our approach connects computational complexity with phase transitions, providing a physical interpretation of quantum speedup. Moreover, it opens the door to exploring potentially useful sampling algorithms on near-term quantum devices as the algorithm for sampling from independent sets on certain graphs can be naturally implemented using Rydberg atom arrays.
Cite
@article{arxiv.2005.14059,
title = {Quantum Sampling Algorithms for Near-Term Devices},
author = {Dominik S. Wild and Dries Sels and Hannes Pichler and Cristian Zanoci and Mikhail D. Lukin},
journal= {arXiv preprint arXiv:2005.14059},
year = {2021}
}