Machine learning is a powerful tool in finding hidden data patterns for quantum information processing. Here, we introduce this method into the optical readout of electron-spin states in diamond via single-photon collection and demonstrate improved readout precision at room temperature. The traditional method of summing photon counts in a time gate loses all the timing information crudely. We find that changing the gate width can only optimize the contrast or the state variance, not both. In comparison, machine learning adaptively learns from time-resolved fluorescence data and offers the optimal data processing model that elaborately weights each time bin to maximize the extracted information. It is shown that our method can repair the processing result from imperfect data, reducing 7% in spin readout error while optimizing the contrast. Note that these improvements only involve recording photon time traces and consume no additional experimental time, and they are, thus, robust and free. Our machine learning method implies a wide range of applications in the precision measurement and optical detection of states.

中文翻译：

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机器学习辅助的金刚石中氮空位中心的电子自旋读数

机器学习是寻找隐藏数据模式以进行量子信息处理的强大工具。在这里，我们将这种方法引入通过单光子收集的金刚石中电子自旋态的光学读出中，并展示了在室温下提高的读出精度。在时间门中对光子计数求和的传统方法会粗略地丢失所有定时信息。我们发现，改变门宽只能优化对比度或状态方差，而不能同时优化两者。相比之下，机器学习可自适应地从时间分辨的荧光数据中学习，并提供优化的数据处理模型，该模型精心地权衡每个时间段，以使提取的信息最大化。结果表明，我们的方法可以从不完善的数据中修复处理结果，在优化对比度的同时，减少了7％的自旋读出误差。请注意，这些改进仅涉及记录光子时间轨迹，而不会消耗额外的实验时间，因此它们是健壮且免费的。我们的机器学习方法在状态的精确测量和光学检测中具有广泛的应用范围。