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Exploring the quantum speed limit with computer games

机译:用电脑游戏探索量子速度极限

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摘要

Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies(1,2). Citizen science (or crowd sourcing) is a way of exploiting this ability by presenting scientific research problems to non-experts. 'Gamification'-the application of game elements in a non-game context-is an effective tool with which to enable citizen scientists to provide solutions to research problems. The citizen science games Foldit(3), EteRNA(4) and EyeWire(5) have been used successfully to study protein and RNA folding and neuron mapping, but so far gamification has not been applied to problems in quantum physics. Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics. We show that human players are able to find solutions to difficult problems associated with the task of quantum computing(6). Players succeed where purely numerical optimization fails, and analyses of their solutions provide insights into the problem of optimization of a more profound and general nature. Using player strategies, we have thus developed a few-parameter heuristic optimization method that efficiently outperforms the most prominent established numerical methods. The numerical complexity associated with time-optimal solutions increases for shorter process durations. To understand this better, we produced a low-dimensional rendering of the optimization landscape. This rendering reveals why traditional optimization methods fail near the quantum speed limit (that is, the shortest process duration with perfect fidelity)(7-9). Combined analyses of optimization landscapes and heuristic solution strategies may benefit wider classes of optimization problems in quantum physics and beyond.
机译:人们通过直观地形成简单的低维启发式策略来常规解决巨大的计算复杂性问题(1,2)。公民科学(或众包)是通过向非专家介绍科研问题来利用这种能力的一种方法。 “游戏化”-游戏元素在非游戏环境中的应用-是使公民科学家能够为研究问题提供解决方案的有效工具。公民科学游戏Foldit(3),EteRNA(4)和EyeWire(5)已成功用于研究蛋白质和RNA折叠以及神经元作图,但到目前为止,游戏化还没有应用于量子物理学中的问题。在这里,我们报道了量子移动,这是一个在线平台,将量子物理学中的优化问题赌博化。我们证明人类玩家能够找到与量子计算任务相关的难题的解决方案(6)。纯粹的数值优化失败的地方,玩家会成功,而他们的解决方案分析将为我们提供更为深刻和笼统的优化问题的见识。因此,使用参与者策略,我们开发了一种几参数启发式优化方法,该方法有效地胜过了最著名的已建立数值方法。与时间最佳解相关的数值复杂性随着过程持续时间的缩短而增加。为了更好地理解这一点,我们制作了优化图的低维渲染。该渲染图揭示了传统的优化方法为何在量子速度极限附近(即具有完美保真度的最短过程持续时间)失败了(7-9)。优化前景和启发式解决方案策略的组合分析可能会有益于量子物理学及其以外的更多种类的优化问题。

著录项

  • 来源
    《Nature》 |2016年第7598期|210-213|共4页
  • 作者单位

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

    Aarhus Univ, Dept Phys & Astron, Aarhus, Denmark;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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