Restricting to the chip architecture maintains the quantum neural network accuracy

arXiv:2212.14426v2 Announce Type: replace-cross
Abstract: In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum facet is characterized by a parametrization $U$, typically derived from the composition of various quantum gates. On the other hand, the classical component involves an optimizer that adjusts the parameters of $U$ to minimize a cost function $C$. Despite the …

abstract accuracy algorithms architecture arxiv chip cs.ai cs.lg devices facet intermediate machine machine learning machine learning models network neural network quant-ph quantum quantum neural network scale strategy type