Feature Map for Quantum Data in Classification
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911899324514304 |
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| author | Kwon, Hyeokjea Lee, Hojun Bae, Joonwoo |
| author_facet | Kwon, Hyeokjea Lee, Hojun Bae, Joonwoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15665 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Feature Map for Quantum Data in Classification Kwon, Hyeokjea Lee, Hojun Bae, Joonwoo Quantum Physics 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. |
| title | Feature Map for Quantum Data in Classification |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2303.15665 |