Feature Map for Quantum Data in Classification

• URL: http://arxiv.org/abs/2303.15665v2
• Date: Mon, 3 Jun 2024 05:56:32 GMT
• Title: Feature Map for Quantum Data in Classification
• Authors: Hyeokjea Kwon, Hojun Lee, Joonwoo Bae,
• Abstract summary: A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms.
• Score: 2.2940141855172036
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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