Variational Optimization for Quantum Problems using Deep Generative Networks

arXiv:2404.18041v1 Announce Type: cross
Abstract: Optimization is one of the keystones of modern science and engineering. Its applications in quantum technology and machine learning helped nurture variational quantum algorithms and generative AI respectively. We propose a general approach to design variational optimization algorithms based on generative models: the Variational Generative Optimization Network (VGON). To demonstrate its broad applicability, we apply VGON to three quantum tasks: finding the best state in an entanglement-detection protocol, finding the ground state of a 1D …

abstract algorithms applications arxiv cs.lg deep generative networks design engineering general generative generative models machine machine learning math.oc modern networks optimization quant-ph quantum quantum technology science technology type