Previously proposed quantum algorithms for solving linear systems of equations cannot be
implemented in the near term due to the required circuit depth. Here, we propose a hybrid …

Developing scalable quantum algorithms to study finite-temperature physics of quantum
many-body systems has attracted considerable interest due to recent advancements in …

This article aims to investigate how circuit-based hybrid quantum convolutional neural
networks (QCNNs) can be successfully employed as image classifiers in the context of …

Abstract Despite advances in Deep Learning, the Convolutional Neural Networks methods
still manifest limitations in medical applications because datasets are usually restricted in …

A quantum error mitigation technique based on machine learning is proposed, which learns
how to adjust the probabilities estimated by measurement in the computational basis. Neural …

Arithmetic plays a major role in a computer? s performance and efficiency. Building new
computing platforms supported by the traditional binary arithmetic and silicon-based …

Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT)
treatment responses, motivating the need for a more personalized treatment plan …

Contribution: In this study, an alternative educational approach for introducing quantum
computing to a wider audience is highlighted. The proposed methodology considers …

Artificial neural networks have been proposed as potential algorithms that could benefit from
being implemented and run on quantum computers. In particular, they hold promise to …

Circuit synthesis is the task of decomposing a given logical functionality into a sequence of
elementary gates. It is (depth-) optimal if it is impossible to achieve the desired functionality …