Adaptive directional gradients for parameterized quantum circuits
Researchers have developed a new framework for estimating gradients in parameterized quantum circuits (PQCs) that significantly reduces the measurement cost associated with training. This approach, based on the forward mode of automatic differentiation, offers an unbiased gradient estimator by averaging random directional derivatives. The proposed QUIVER optimizer, derived from this framework, demonstrates orders of magnitude greater efficiency in training quantum neural networks compared to the standard parameter-shift rule, outperforming other measurement-frugal optimizers on various quantum algorithms. AI
IMPACT This new gradient estimation technique could accelerate the development and application of quantum machine learning models.