Quantum Natural Gradient

Quantum Natural Gradient (QNG) methods aim to accelerate the optimization of variational quantum algorithms (VQAs) by leveraging the inherent geometry of the quantum state space, improving upon standard gradient descent approaches. Current research focuses on developing efficient QNG algorithms, such as momentum-enhanced versions and those employing techniques like pairwise coordinate descent, often incorporating advanced gradient estimation methods (e.g., generalized Hadamard tests) to reduce computational cost. These advancements are crucial for tackling challenges like barren plateaus in VQA training and are being experimentally validated on platforms like photonic quantum computers, demonstrating potential for faster convergence and improved performance in applications such as quantum chemistry and machine learning.