Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

One of the most promising areas of research to obtain practical advantage is Quantum
Machine Learning which was born as a result of cross-fertilisation of ideas between …

Image classification, a pivotal task in multiple industries, faces computational challenges
due to the burgeoning volume of visual data. This research addresses these challenges by …

Simple Summary This work successfully employs a novel approach in processing patient
and drug data to predict the drug response for cancer patients. The approach uses a deep …

Powerful hardware services and software libraries are vital tools for quickly and affordably
designing, testing, and executing quantum algorithms. A robust large‐scale study of how the …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …

Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of
industrial activities. Use cases span many applications, from weather forecasting to digital …

Image recognition is one of the primary applications of machine learning algorithms.
Nevertheless, machine learning models used in modern image recognition systems consist …

Quantum machine learning has become an area of growing interest but has certain
theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or …

The use of quantum neural networks for machine learning is a paradigm that has recently
attracted considerable interest. Under certain conditions, these models approximate the …