Rapid Classification of Quantum Sources Enabled by Machine Learning

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Track Code	2021-BOLT-69214
Categories	Computer Technology Electrical Engineering
Keywords	Algorithm Big Data Chemical Analysis Classification Technology Communications and Computing Computer Technology Cybersecurity Data Data Processing Data Science Deterministic Optical Quantum Computing Electrical Engineering Machine Learning Measurements Photons Physics Quantum Quantum Communication Quantum Computation Quantum Optics
Public Abstract	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. Advantages: -Rapid Classification -Highly Accurate -High-Resolution Imaging -Reliable Potential Applications: -Quantum Computing -Quantum Communication -Cryptography -Quantum Simulations -Low-Light Sensing -Quantum Random Number Generation -Magnetic-Field Sensing -Quantum Measurements and Standards Technology Validation: 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. Recent Publication: "Rapid Classification of Quantum Sources Enabled by Machine Learning" Journal of Advanced Quantum Technology DOI: 10.1002/qute.202000067
People	Boltasseva, Alexandra (Project leader) Bogdanov, Simeon I. Isacsson, Theodor Kildishev, Alexander V. Kudyshev, Zhaxylyk Shalaev, Vladimir M
Intellectual Property	Application Date Jul 6, 2022 Type Utility-Gov. Funding Country of Filing United States Patent Number (None) Issue Date (None) Application Date Dec 2, 2021 Type Provisional-Gov. Funding Country of Filing United States Patent Number (None) Issue Date (None) Application Date Nov 15, 2020 Type Provisional-Patent Country of Filing United States Patent Number (None) Issue Date (None)
Contact OTC	Purdue Office of Technology Commercialization The Convergence Center 101 Foundry Drive, Suite 2500 West Lafayette, IN 47906 Phone: (765) 588-3475 Fax: (765) 463-3486 Email: otcip@prf.org