4.6 Article

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

Journal

APPLIED PHYSICS LETTERS
Volume 118, Issue 8, Pages -

Publisher

AMER INST PHYSICS
DOI: 10.1063/5.0038590

Keywords

-

Funding

  1. National Natural Science Foundation of China [11904070, 11604069, 61805064]
  2. National Key R&D Program of China [2020YFA0309400, 2018YFA0306600, 2018YFF01012500]
  3. Fundamental Research Funds for the Central Universities [PA2019GDQT0023]

Ask authors/readers for more resources

Machine learning method can optimize the precision of light readout and extract information, providing the best data processing model by learning time-resolved fluorescence data, reducing readout errors and optimizing contrast.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available