Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks

arXiv:2403.07025v1 Announce Type: cross
Abstract: In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach to ameliorate this challenge by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations. By employing the Qiskit …

abstract accuracy algorithm algorithms arxiv computing cs.lg devices however intermediate networks neural networks nisq noise quant-ph quantum quantum computing scale type via