  The paper [S{\o}rensen et al., Nature 532] considers how human players
compare to algorithms for solving the Quantum Moves game BringHomeWater and
design new algorithms based on the intuition extracted from players. The claim
by [S{\o}rensen et al., Nature 532] is that players outperform widely used
algorithms, in particular the KASS algorithm, based on the Krotov algorithm,
and that player intuition is crucial to develop improved methods. However, as
initially discussed by D. Sels [D. Sels, Phys. Rev. A 97], a standard
Coordinate Ascent algorithm outperforms all players by a large margin. Albeit
D. Sels only compare to player solutions, the simple algorithm outperforms all
algorithms based on player solutions and Krotov, and it does so using much less
time and iterations. In this paper we elaborate on the methods discussed by D.
Sels and verify that the presented algorithm, solves the problem better than
all players and algorithms derived from player solutions in [S{\o}rensen et
al., Nature 532]. We also verify the theoretical analysis presented by D. Sels,
that gives a theoretically derived protocol that outperforms all players. We
add a comparison with gradient ascent or GRAPE. Starting from uniform random
values, GRAPE outperforms all players by a large margin. GRAPE works at least
as well as the methods from [S{\o}rensen et al., Nature 532] initialized with
player solutions. A standard analysis of the results from GRAPE provides a
starting point for GRAPE, that outperform all algorithms from [S{\o}rensen et
al., Nature 532]. We compare with a basic Krotov algorithm, and get results
similar to GRAPE, clearly outperforming players and the KASS algorithm. These
experiments verify and underline the result in [D. Sels, Phys. Rev. A 97] that
the conclusions from [S{\o}rensen et al., Nature 532] regarding algorithms are
untenable. In fact the opposite conclusions are true.
