The paper [S{\o}rensen et al., Nature 532] presents and discusses results for a Quantum Moves game , BringHomeWater , where players have attempted to move a quantum state from one position to another in a simulated optical tweezer and atoms setup. The paper compares the player solutions to numerical methods that the authors discuss. In particular, [S{\o}rensen et al., Nature 532] show evidence that ] the so-called Krotov optimization method performs worse than solutions that the human players have come up with. Given the assumption that the Krotovmethod has been correctly applied, the evidence points to the fact that human players can outperform the Krotov method. This is the find and claim of [S{\o}rensen et al., Nature 532] and it features prominent in the abstract of the paper. It leads the authors to conclude that 'playerssucceed where purely numerical optimization fails, and analysis of their solutions provide insights into the problem of optimization of a more profound and general nature. ' While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [S{\o}rensen et al., Nature 532] , as a consequence also algorithms like CRAB and evolutionary approaches which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance. In fact, as has been discussed first by D. Sels [ D.Sels, Phys. Rev. A 97, 040302 , a very simple approach using classical arguments can capture the BringHomeWater Quantum Moves game far better than the player approach. Furthermore, as also shown by }  [D. Sels, Phys. Rev. A 97 , 040302 ] , one of the simplest optimization algorithms available, Stochastic Ascent, can outperform all of the above. Here we elaborate on the method discussed by [ D.Sels, Phys. Rev. A 97 } , 040302 ] and verify the conclusions put forward by D.Sels
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.
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