In this theoretical work we study three theorems that establish limitations to three basic processes in thermodynamics: an adiabatic process, an erasure process and a work extraction process. We propose a theoretical setting ...

Diffusion refers to numerous phenomena, by which particles and bodies of all kinds move throughout any kind of material, has emerged as one of the most prominent subjects in the study of complex systems. Motivated by the ...

In the past years Machine Learning has shown to be a useful tool in quantum many-body physics to detect phase transitions. Being able to identify phases via machine learning introduces the question of how did the algorithm ...

The last decades have witnessed impressive technical advances in all the fields of quantum science, including solid-state systems or atomic, molecular, and optical physics, allowing one to control materials at the microscopic ...

At sufficiently low temperatures, fluids of bosonic particles may undergo a phase transition, below which a Bose-Einstein condensate (BEC) forms. The BEC is one of the most fundamental concepts in many-body quantum physics ...

During the past two decades, thanks to the mutual fertilization of the research in quantum information and condensed matter, new approaches based on purely quantum features without any classical analog turned out to be ...

In this thesis, we report experimental evidence of a "gray" condensate of excitons, as predicted theoretically by M. Combescot et al. Most importantly, the condensate is characterized by the macroscopic population of dark ...

During the last years, machine learning techniques have been applied todiscover properties of physical systems. However, these algorithms are black boxesand there is a need for tools to interpret them. ...

(English) We perform quantum simulation on classical and quantum computers and set up a machine learning framework in which we can map out phase diagrams of known and unknown quantum many-body systems in an unsupervised ...