Jordan, S.
, Jarret, M.
and Lackey, B.
(2016),
Adiabatic Optimization versus Diffusion Monte Carlo, Physical Review A
(Accessed September 10, 2024)
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Adiabatic Optimization versus Diffusion Monte Carlo
Published
Author(s)
, Michael Jarret, Brad Lackey
Abstract
Most experimental and theoretical studies of adiabatic optimization use stoquastic Hamiltonians, whose ground states are expressible using only real nonnegative amplitudes. This raises a question as to whether classical Monte Carlo methods can simulate stoquastic adiabatic algorithms with polynomial overhead. Here, we analyze diffusion Monte Carlo algorithms. We argue that, based on differences between L1 and L2 normalized states, these algorithms suffer from certain obstructions preventing them from efficiently simulating stoquastic adiabatic evolution in generality. In practice however, we obtain good performance by introducing a method that we call Sub-Stochastic Monte Carlo. In fact, our simulations are good classical optimization heuristics in their own right, competitive with the best previously known heuristic solvers for MAX-k-SAT at k = 2,3,4.Citation
Physical Review A
Volume
94
Pub Type
Journals
Keywords
quantum computing, optimization, adiabatic, Monte Carlo
Citation
Issues
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Created October 13, 2016, Updated February 19, 2017