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arXiv 2102.08558quant-ph

Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

Peng Qian, Xue Lin, Feifei Zhou, Runchuan Ye, Yunlan Ji, Bing Chen, Guangjun Xie, Nanyang Xu

英文摘要:

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, they are thus robust and free. Our machine learning method implies a wide range of applications in precision measurement and optical detection of states.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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