Efficient Gradient Estimation of Variational Quantum Circuits with Lie Algebraic Symmetries

arXiv:2404.05108v1 Announce Type: cross
Abstract: Hybrid quantum-classical optimization and learning strategies are among the most promising approaches to harnessing quantum information or gaining a quantum advantage over classical methods. However, efficient estimation of the gradient of the objective function in such models remains a challenge due to several factors including the exponential dimensionality of the Hilbert spaces, and information loss of quantum measurements. In this work, we study generic parameterized circuits in the context of variational methods. We develop a …

abstract arxiv challenge circuits cs.it cs.lg function gradient however hybrid information math.it optimization quant-ph quantum quantum advantage strategies type