A Novel Image Classification Framework Based on Variational Quantum Algorithms

arXiv:2312.07932v2 Announce Type: replace-cross
Abstract: Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs)-hybrid …

abstract algorithms applications arxiv classification complexity computational cs.ai cs.cv framework global however image machine machine learning network novel overfitting pooling practical quant-ph quantum reduce results the end type