论文标题
基于钻石的量子应用的机器和量子学习
Machine and quantum learning for diamond-based quantum applications
论文作者
论文摘要
近年来，机器和量子学习已经获得了计算能力和数据可用性增长所维持的巨大动力，并且显示出了解决识别识别和分类型问题的非凡恰当性，以及需要复杂，战略计划的问题。在这项工作中，我们讨论并分析了在基于钻石的量子技术的开发中扮演的角色机器和量子学习。这很重要，因为Diamond及其光学可调的旋转缺陷正成为量子信息，计算和计量学中固态应用程序的主要硬件候选。通过选定的演示数量，我们表明机器和量子学习正在导致测量速度和准确性的实际和基本改进。这对于量子应用至关重要，尤其是对于那些相干时间和信噪比比率很少的资源的应用程序。我们总结了一些最突出的机器和量子学习方法，这些方法有利于提出的进步，并讨论了其提出的和未来的量子应用的潜力。
In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically-addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential for proposed and future quantum applications.
