Fourier series weight in quantum machine learning
- URL: http://arxiv.org/abs/2302.00105v2
- Date: Mon, 26 Feb 2024 17:24:29 GMT
- Title: Fourier series weight in quantum machine learning
- Authors: Parfait Atchade-Adelomou and Kent Larson
- Abstract summary: We will propose models, tests, and demonstrations to achieve this objective.
We designed a quantum machine learning leveraged on the Hamiltonian encoding.
We performed and tested all the proposed models using the Pennylane framework.
- Score: 2.179493184917471
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we aim to confirm the impact of the Fourier series on the
quantum machine learning model. We will propose models, tests, and
demonstrations to achieve this objective. We designed a quantum machine
learning leveraged on the Hamiltonian encoding. With a subtle change, we
performed the trigonometric interpolation, binary and multiclass classifier,
and a quantum signal processing application. We also proposed a block diagram
of determining approximately the Fourier coefficient based on quantum machine
learning. We performed and tested all the proposed models using the Pennylane
framework.
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