Identification of a suitable source of single photons via second-order autocorrelation function measurement within an array of thousand possible candidates is a routine, key step of any practical realization in quantum optics. Within this work, we have shown that machine learning algorithms enable high precision classification between “single” and “not single” quantum emitters based on sparse autocorrelation data measurement and require < 1 s acquisition time, while conventional methods demand > 1 min. Machine learning assisted classification, done on a sparse 1-s dataset, provides ~85% accuracy of “single”/“not single” emitter identification versus only 57% of the conventional Levenberg-Marquardt approach.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.