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.
You can find information on related articles in the link below.
Congratulations again on Professor June-Koo Rhee’s research team for their outstanding performance in the field of quantum computing.