|Researchers at Purdue University have developed a new method for rapid classification of quantum scores allowing a computer can retrieve quantum super-resolution imaging using machine learning. Several algorithms for image optimization have been integrated in this machine learning method to reduce time delays in imaging and achieve exceptional image quality, allowing image collection to be completed on average within one second and consistently in less than two minutes. Purdue researchers can quickly determine "good" and "bad" emitters with 92% accuracy are obtained in this experiment. This technology can be used in myriad applications including quantum computation, quantum communication, cryptography, quantum simulations, quantum random number generation, low-light sensing, magnetic-field sensing, quantum-enhanced measurements, and quantum standards.
-Quantum Random Number Generation
-Quantum Measurements and Standards
In a series of 41 potential emitters, 15 were found to be single emitters and 26 were found not to be single emitters, allowing researchers to sift through large volumes of data and classify emitters as "good" or "bad" with 92% accuracy in less than two minutes, often less than 1 second.
"Rapid Classification of Quantum Sources Enabled by Machine Learning"
Journal of Advanced Quantum Technology
Nov 15, 2020
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