Improving gradient methods via coordinate transformations: Applications to quantum machine learning

In this paper, we introduce a generic strategy to accelerate and improve the overall performance of machine-learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent. The overall performance is dependent on the appearance of local minima and barren plateaus, which slow down calculations and lead to nonoptimal solutions. In practice, this results in dramatic computational and energy costs for artificial intelligence applications. Our method is based on coordinate transformations, like variational rotations, adding extra directions in parameter space that depend on the cost function itself, and which allowus to explore the configuration landscape more efficiently. The validity of our method is benchmarked by boosting several quantum machine-learning algorithms, getting a very significant improvement in their performance. Published by the American Physical Society 2024