Benchmarking Variants of the Adam Optimizer for Quantum Machine Learning Applications

Quantum Machine Learning is gaining traction by leveraging quantum advantage to outperform classical Machine Learning. Many classical and quantum optimizers have been proposed to train Parameterized Quantum Circuits in the simulation environment, achieving high accuracy and fast convergence speed. H...

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Bibliographic Details

Published in:	IEEE Open Journal of the Computer Society
Main Authors:	Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham, Yasuhiko Nakashima
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Optimizer quantum simulator benchmarking variational quantum eigensolver quantum approximate optimization algorithm dynamic simulation
Online Access:	https://ieeexplore.ieee.org/document/11072814/