Improving Gradient Methods via Coordinate Transformations: Applications to Quantum Machine Learning

arXiv:2304.06768v2 Announce Type: replace-cross
Abstract: Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overall performance is dependent on the appearance of local minima and barren plateaus, which slow-down calculations and lead to non-optimal solutions. In practice, this results in dramatic computational and energy costs for AI applications. In this paper we introduce a generic strategy to accelerate and improve the overall performance of …

abstract algorithms applications arxiv cs.lg gradient improving machine machine learning machine learning algorithms optimization performance quant-ph quantum type versions via