Machine Learning for Efficient Quantum Sources

For quantum optical techno­logies to become more practical, there is a need for large-scale inte­gration of quantum photonic circuits on chips. This inte­gration calls for scaling up key building blocks of these circuits produced by single quantum optical emitters. Purdue University engineers created a new machine learning-assisted method that could make quantum photonic circuit develop­ment more efficient by rapidly preselecting these solid-state quantum emitters.

Trianing a machine to recognize promising patterns in single-photon emission within a split second. (Source: S. Bogdanov, Purdue U.)

Researchers around the world have been exploring different ways to fabricate identical quantum sources by trans­planting nano­structures containing single quantum optical emitters into conven­tional photonic chips. “With the growing interest in scalable reali­zation and rapid proto­typing of quantum devices that utilize large emitter arrays, high-speed, robust preselection of suitable emitters becomes necessary,” said Alexandra Boltas­seva, Purdue’s Ron and Dotty Garvin Tonjes Professor of Electrical and Computer Engi­neering. Quantum emitters produce light with unique, non-classical proper­ties that can be used in many quantum information protocols.

The challenge is that inter­facing most solid-state quantum emitters with existing scalable photonic platforms requires complex inte­gration techniques. Before integrating, engineers need to first identify bright emitters that produce single photons rapidly, on-demand and with a specific optical frequency. Emitter preselection based on single-photon purity – which is the ability to produce only one photon at a time – typically takes several minutes for each emitter. Thousands of emitters may need to be analyzed before finding a high-quality candidate suitable for quantum chip inte­gration.

To speed up screening based on single-photon purity, Purdue researchers trained a machine to recognize promising patterns in single-photon emission within a split second. According to the researchers, rapidly finding the purest single-photon emitters within a set of thousands would be a key step toward practical and scalable assembly of large quantum photonic circuits. “Given a photon purity standard that emitters must meet, we have taught a machine to classify single-photon emitters as suffi­ciently or insuf­ficiently pure with 95 % accuracy, based on minimal data acquired within only one second,” said Zhaxylyk Kudyshev, a Purdue post­doctoral researcher.

The researchers found that the conven­tional photon purity measurement method used for the same task took 100 times longer to reach the same level of accuracy. “The machine learning approach is such a versatile and efficient technique because it is capable of extracting the information from the dataset that the fitting procedure usually ignores,” Boltasseva said. The researchers believe that their approach has the potential to drama­tically advance most quantum optical measurements that can be formulated as binary or multiclass classi­fication problems. “Our technique could, for example, speed up super-reso­lution micro­scopy methods built on higher-order corre­lation measurements that are currently limited by long image acqui­sition times,” Kudyshev said. (Source: Purdue U.)

Reference: Z. A. Kudyshev et al.: Rapid Classification of Quantum Sources Enabled by Machine Learning, Adv. Quant. Tech., online 2 September 2020; DOI: 10.1002/qute.202000067

Link: Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, USA

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