arXiv:2303.15665 · International Conference on Quantum Communications, Networking, and Computing (QCNC)

Feature Map for Quantum Data in Classification

Hyeokjea Kwon, Hojun Lee, Joonwoo Bae

Published Jul 1, 2024• 4 citations

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.

DOI: 10.1109/qcnc62729.2024.00016

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