Rapid Classification of Quantum Sources Enabled by Machine Learning
Abstract
Deterministic nanoassembly may enable unique integrated on‐chip quantum photonic devices. Such integration requires a careful large‐scale selection of nanoscale building blocks such as solid‐state single‐photon emitters by means of optical characterization. Second‐order autocorrelation is a cornerstone measurement that is particularly time‐consuming to realize on a large scale. Supervised machine learning‐based classification of quantum emitters as “single” or “not‐single” is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100‐fold speedup compared to the conventional Levenberg–Marquardt fitting approach. It is anticipated that machine learning‐based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 02, 2020
- Source ID
- 10.1002/qute.202000067
Entities
People
- Alexander V. Kildishev
- Alexandra Boltasseva
- Simeon I Bogdanov
- Theodor Isacsson
- Vladimir Shalaev
- Zhaxylyk A Kudyshev
Organizations
- National Science Foundation
- Purdue University
- Royal Institute of Technology
- United States Department of Energy