通过量子退火扩展自动化超材料发现的视野
Expanding the horizon of automated metamaterials discovery via quantum annealing
摘要：
通过机器学习设计的材料的复杂性目前受到经典计算机效率低下的限制。我们展示了如何将量子退火纳入自动材料发现，并对设计由SiO2，SiC和聚甲基丙烯酸甲酯组成的复杂热功能超材料进行了原理验证研究。我们的量子经典混合算法的经验计算时间，涉及因式分解机，严格的耦合波分析，和D-Wave 2000 Q量子退火是不敏感的问题的大小，而经典的对应经历了快速增加。我们的方法被用来设计复杂的波长选择性辐射器的结构，与现有的人类设计的替代品相比，显示出更好的一致性与热大气透明度窗口。我们的研究结果表明，量子退火为科学家提供了巨大的计算能力，可能会改变材料的设计方式。
英文摘要：
Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed.
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