Image classification is a crucial task in machine learning with widespread practical
applications. The existing classical framework for image classification typically utilizes a …

Quantum neural network fully utilize the respective advantages of quantum computing and
classical neural network, providing a new path for the development of artificial intelligence …

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine
learning (ML) architectures for handling multidimensional data by maintaining data locality …

This work investigates the application of quantum machine learning techniques for classical
and quantum communication across different qubit channel models. By employing …

A common model for classifying images is the convolutional neural network (CNN), which
has the benefit of effectively using data correlation information. Despite their remarkable …

Quanvolutional neural networks (QNNs) have been successful in image classification,
exploiting inherent quantum capabilities to improve performance of traditional convolution …

In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN)
that is capable to perform uncertainty-aware classification of classical medical dataset. This …

Quantum machine learning was recently applied to various applications and leads to results
that are comparable or, in certain instances, superior to classical methods, in particular …

Grover adaptive search (GAS) for binary optimization (BO) problems is a quantum algorithm
for iteratively finding optimal solutions using Grover's search algorithm. However, in GAS …

Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant
technology tool for image processing and human health. CNNs, which mimic the working …