Time: 11:00, Thursday, Feb. 20, 2025 
Speaker: Mr. Hyeokjea Kwon (Ph.D Student)
                School of Electrical Engineering, Korea Advanced Institute of Science and Technology, South Korea 
Title: Feature map for quantum data in classification
Place: Meeting Room, 2F, QFort, NCKU 

Abstract:
The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. In this work, we point out that the quantum state space is specific such that a measurement postulate characterizes an inner product and that manipulation of quantum states prepared from classical data cannot enhance the distinguishability of data points. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms.