The paper [ sorensen16 } ] 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, [ sorensen16 } ] show evidence that the so-called Krotov optimization method krotov performs worse than solutions that the human players have come up with. Given the assumption that the Krotov method 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 [ sorensen16 } ] and it features prominent in the abstract of the paper. It leads the authors to conclude that 'players succeed 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 [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve 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 [ DSels ], 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 [ DSels ], one of the simplest optimization algorithms available, Stochastic Ascent, can outperform all of the above. Here we elaborate on the method discussed by [ DSels ] and verify the conclusions put forward by D. Sels
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 Krotov method 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 'players succeed 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
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D. Sels, Phys. Rev. A 97, 040302
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D. Sels, Phys. Rev. A 97, 040302