Fast Quantum Process Tomography via Riemannian Gradient Descent

arXiv:2404.18840v1 Announce Type: cross
Abstract: Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. In this paper, we introduce a modified version of stochastic gradient descent on a Riemannian manifold that integrates recent advancements in numerical …

abstract arxiv cs.lg fields gradient information issue optimization physics process quant-ph quantum role science type via