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Fourier series Weight in Quantum Machine Learning

In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model.

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Fourier series Weight in Quantum Machine Learning

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  1. Fourier series Weight in Quantum Machine Learning Original Research (Published On: 04-Feb-2024 ) Fourier Series Weight in Quantum Machine Learning DOI : https://dx.doi.org/10.54364/AAIML.2024.41108 Parfait Atchade Adelomou Adv. Artif. Intell. Mach. Learn., 4 (1):1866-1890 Parfait Atchade Adelomou : City Science Group MIT Media Lab E15-368 20 Ames Street, E15, Cambridge, MA 02139 --------------------- Smart Society Research Group - La Salle - Universitat Ramon Llull, Carrer de Sant Joan de La Salle, 42, 08022 Barcelona (Spain) 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. Article History: Received on: 27-Dec-23, Accepted on: 28-Jan-24, Published on: 04-Feb-24 Corresponding Author: Parfait Atchade Adelomou Welcome to the Advances in Artificial Intelligence and Machine Learning: An Machine Learning Journal . Advances in Artificial Intelligence and Machine Learning (oajaiml) is a Journal, that publishes recent advancements in the Artificial Intelligence, Machine Learning and applications related to it.

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