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 …

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 …

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

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 …

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

Quantum computing is aninterdisciplinary field that lies at the intersection of mathematics,
quantum physics, and computer science, and finds applications in areas including …

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

The intractability of deterministic solutions in solving\(\mathcal {NP}\)-Hard Combinatorial
Optimisation Problems (COP) is well reported in the literature. One mechanism for …

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 …