Fourier series weight in quantum machine learning
This work addresses the role of Fourier series in quantum machine learning, but it appears incremental as it focuses on confirming and applying existing concepts without introducing major new breakthroughs.
The authors investigated the impact of Fourier series on quantum machine learning models by designing models based on Hamiltonian encoding, performing tasks like trigonometric interpolation and classification, and proposing a block diagram for approximating Fourier coefficients, all tested using the Pennylane framework.
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.