New machine learning-assisted method rapidly classifies quantum sources


Date:
September 10, 2020
Source:
Purdue University
Summary:
Engineers have created a new machine learning-assisted method that
could make quantum photonic circuit development more efficient by
rapidly pre- selecting these solid-state quantum emitters.



FULL STORY ==========================================================================
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 - - sources of particles of light -- 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.

The work is published in the journal Advanced Quantum Technologies.

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.


========================================================================== Story Source: Materials provided by Purdue_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Zhaxylyk A. Kudyshev, Simeon I. Bogdanov, Theodor Isacsson,
Alexander V.

Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev. Rapid
Classification of Quantum Sources Enabled by Machine
Learning. Advanced Quantum Technologies, 2020; 2000067 DOI:
10.1002/qute.202000067 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200910171826.htm

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