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
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