Once developed for quantum theory, tensor networks (TNs) have been established as a
successful machine learning (ML) paradigm. Now, they have been ported back to the …

The recent literature reveals a dichotomy formed by a coevolution between cryptography
and Artificial Intelligence (AI). This dichotomy consists of two sides, namely Crypto …

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

Clustering is one of the most frequent problems in many domains, in particular, in particle
physics where jet reconstruction is central in experimental analyses. Jet clustering at the …

Simulating the real-time dynamics of gauge theories represents a paradigmatic use case to
test the hardware capabilities of a quantum computer, since it can involve non-trivial input …

The high energy physics (HEP) community has a long history of dealing with large-scale
datasets. To manage such voluminous data, classical machine learning and deep learning …

It is a hot topic how entanglement, a quantity from quantum information theory, can assist
machine learning. In this work, we implement numerical experiments to classify …

We describe a quantum-assisted machine learning method in which multivariate data are
encoded into quantum states in a Hilbert space whose dimension is exponentially large in …

Noisy, intermediate-scale quantum (NISQ) computing devices have become an industrial
reality in the last few years, and cloud-based interfaces to these devices are enabling the …

Quantum machine learning (QML) models promise to have some computational (or
quantum) advantage for classifying supervised datasets (eg, satellite images) over some …