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Markov chain Monte Carlo algorithms have important applications in counting problems and in machine learning problems, settings that involve estimating quantities that are difficult to compute exactly.
Quantum speedup for finding marked vertices by quantum walks, 2019, arXiv:1903.07493
A. Ambainis, A. Gilyen, S. Jeffery, and M. Kokainis · 1903
Earlier work this paper cites.
Some inequalities for reversible Markov chains
D. Aldous · 1982
Earlier work this paper cites.
Random generation of combinatorial structures from a uniform distribution
M. Jerrum, L. Valiant, and V. Vazirani · 1986
Earlier work this paper cites.
Approximating the permanent
M. Jerrum and A. Sinclair · 1989
Earlier work this paper cites.
A random polynomial time algorithm for approximating the volume of convex bodies
M. Dyer, A. Frieze, and R. Kanna · 1991
Earlier work this paper cites.
Approach to equilibrium of Glauber dynamics in the one phase region
F. Martinelli and E. Olivieri · 1994
Earlier work this paper cites.
A very simple algorithm for estimating the number of k k -colorings of a low-degree graph
M. Jerrum · 1995
Earlier work this paper cites.
Efficient simulation of quantum systems by quantum computers
C. Zalka · 1998
Earlier work this paper cites.
A note on the glauber dynamics for sampling independent sets
E. Vigoda · 2001
Earlier work this paper cites.
Quantum Amplitude Amplification and Estimation
G. Brassard, P. Høyer, M. Mosca, and A. Tapp · 2002
Earlier work this paper cites.
Creating superpositions that correspond to efficiently integrable probability distributions, 2002, arXiv:quant-ph/0208112
L. Grover and T. Rudolph · 2002
Earlier work this paper cites.
Adiabatic quantum state generation and statistical zero knowledge
D. Aharonov and A. Ta-Shma · 2003
Cited alongside, same era.
A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
M. Jerrum, A. Sinclair, and E. Vigoda · 2004
Cited alongside, same era.
Quantum Networks for Generating Arbitrary Quantum States
P. Kaye and M. Mosca · 2004
Cited alongside, same era.
Quantum speed-up of Markov chain based algorithms
M. Szegedy · 2004
Cited alongside, same era.
Quantum arthur-merlin games
C. Marriott and J. Watrous · 2005
Cited alongside, same era.
Quantum speedup of classical mixing processes
P. C. Richter · 2007
Cited alongside, same era.
K. Temme, T. J. Osborne, K. G. Vollbrecht, D. Poulin, and F. Verstraete · 2011
Later among the works it cites.
Approximation algorithms for the normalizing constant of Gibbs distributions
M. Huber · 2012
Later among the works it cites.
A quantum–quantum metropolis algorithm
M.-H. Yung and A. Aspuru-Guzik · 2012
Later among the works it cites.
Exact thresholds for Ising–Gibbs samplers on general graphs
E. Mossel, A. Sly, et al · 2013
Later among the works it cites.
M. Ozols, M. Roetteler, and J. Roland · 2013
Later among the works it cites.
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Some settings supporting efficient state preparation
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