In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Estimation of physical quantities is at the core of most scientific research, and the use of
quantum devices promises to enhance its performances. In real scenarios, it is fundamental …

The study of many-body quantum dynamics in strongly correlated systems is extremely
challenging. To date, few numerical methods exist that are capable of simulating the non …

Entanglement is a quintessential quantum mechanical phenomenon with no classical
equivalent. First discussed by Einstein, Podolsky, and Rosen and formally introduced by …

Deep generative models are key-enabling technology to computer vision, text generation,
and large language models. Denoising diffusion probabilistic models (DDPMs) have …

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the
principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of …

Despite fundamental interests in learning quantum circuits, the existence of a
computationally efficient algorithm for learning shallow quantum circuits remains an open …

Generalization is the ability of machine learning models to make accurate predictions on
new data by learning from training data. However, understanding generalization of quantum …

Understanding quantum systems is of significant importance for assessing the performance
of quantum hardware and software, as well as exploring quantum control and quantum …