Professor June-Koo Rhee’s research team developed a non-linear quantum machine-learning artificial intelligence algorithm through collaborative research with German and South African research teams.
Through this study, a non-linear kernel was devised to enable quantum machine learning of complex data. In particular, the quantum supervised learning algorithm developed by Professor June-Koo Rhee’s research team can be calculated with a minimal amount of computation. Therefore, the algorithm presents the possibility of overtaking current AI technologies that require large amounts of computation.
Professor June-Koo Rhee’s research team developed quantum forking technology that generates train and test data through quantum information and enables parallel computation of quantum information. A simple quantum measurement technique has been combined to create a quantum algorithm system that implements non-linear kernel-based supervised learning that efficiently calculates similarities between quantum data. The research team successfully demonstrated quantum supervised learning on real quantum computers through IBM cloud services. Research professor Kyung-Deock Park (KAIST) participated as the first author. The result of this study was published in the 6th volume of May 2020, ‘npj Quantum Information’, a sister journal of the international journal Nature. (Title: Quantum classifier with tailored quantum kernel).
Furthermore, the research team theoretically proved that it is possible to implement various quantum kernels through the systematic design of quantum circuits. In kernel-based machine learning, the optimal kernel may vary depending on the given input data. Therefore, being able to implement various quantum kernels efficiently is a significant achievement in the practical application of quantum kernel-based machine learning.
Research professor Kyung-Deock Park said, “The kernel-based quantum machine learning algorithm developed by the research team will surpass traditional kernel-based supervised learning in the era of hundreds of qubits of Noisy Intermediate-Scale Quantum (NISQ) computing, which is expected to be commercialized in the next few years. The developed algorithm will be actively used as a quantum machine learning algorithm for pattern recognition of complex non-linear data.”
Meanwhile, this research was carried out with the support of the Korea Research Foundation’s Creative Challenge Research Foundation Support Project, the Korea Research Foundation’s Korea-Africa Cooperation Foundation Project, and the Information and Communication Technology Expert Training Project (ITRC) supported by the Institute for Information and Communications Technology Promotion.
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Congratulations again on Professor June-Koo Rhee’s research team for their outstanding performance in the field of quantum computing.
