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

TL;DR

This paper demonstrates that machine learning can significantly enhance the precision of electron-spin state readout in diamond NV centers by utilizing time-resolved fluorescence data, outperforming traditional photon counting methods.

Contribution

The study introduces a machine learning approach for optical spin readout that adaptively weights time-bin data, improving accuracy without additional experimental time.

Findings

Reduced spin readout error by 7% using machine learning.

Optimized contrast and variance through adaptive time-bin weighting.

Method is robust, cost-free, and applicable to quantum sensing.

Abstract

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.