Abstract Machine learning has been used in high energy physics (HEP) for a long time,
primarily at the analysis level with supervised classification. Quantum computing was …

Neural networks are computing models that have been leading progress in Machine
Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale …

In the noisy intermediate-scale quantum (NISQ) era, the computing power displayed by
quantum computing hardware may be more advantageous than classical computers, but the …

Estimating the difference between quantum data is crucial in quantum computing. However,
as typical characterizations of quantum data similarity, the trace distance and quantum …

Tensor networks (TN) are approximations of high-dimensional tensors designed to
represent locally entangled quantum many-body systems efficiently. This paper provides a …

One-class classification (OCC) is a fundamental problem in pattern recognition with a wide
range of applications. This work presents a semi-supervised quantum machine learning …

The resurgence of self-supervised learning, whereby a deep learning model generates its
own supervisory signal from the data, promises a scalable way to tackle the dramatically …

In-depth theoretical and practical research is nowadays being performed on variational
quantum algorithms (VQAs), which have the potential to surpass traditional, classical …

Novel uses of graphical processing units for accelerated computation revolutionized the field
of high-performance scientific computing by providing specialized workflows tailored to …

The rapid advancements in machine learning and quantum computing have given rise to a
new research frontier: quantum machine learning. Quantum models designed for tackling …