In the last decade, quantum simulators, and in particular cold atoms in optical lattices, have
emerged as a valuable tool to study strongly correlated quantum matter. These experiments …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

Nano-color routing has emerged as an immensely popular and widely discussed subject in
the realms of light field manipulation, image sensing, and the integration of deep learning …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

Machine learning has recently emerged as a promising approach for studying complex
phenomena characterized by rich datasets. In particular, data-centric approaches lead to the …

Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …

Current quantum simulation experiments are starting to explore nonequilibrium many-body
dynamics in previously inaccessible regimes in terms of system sizes and timescales …

Quantum simulators offer powerful means to investigate strongly correlated quantum matter.
However, interpreting measurement outcomes in such systems poses significant challenges …

Parametrized quantum circuits are widely used for supervised learning tasks such as image
classification in the noisy intermediate scale quantum era. However, normally, it can only …

In the study of closed many-body quantum systems, one is often interested in the evolution
of a subset of degrees of freedom. On many occasions it is possible to approach the problem …