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 …

Classical machine learning has succeeded in the prediction of both classical and quantum
phases of matter. Notably, kernel methods stand out for their ability to provide interpretable …

In the systems showing critical behavior, various response functions have a singularity at the
critical point. Therefore, as the driving field is tuned toward its critical value, the response …

Classification and identification of different phases and the transitions between them is a
central task in condensed matter physics. Machine learning, which has achieved dramatic …

Despite the widespread use and success of machine-learning techniques for detecting
phase transitions from data, their working principle and fundamental limits remain elusive …

We explore the statistical properties of energy transfer in ensembles of doubly driven
random-matrix Floquet Hamiltonians based on universal symmetry arguments. The energy …

In ultracold-atom experiments, data often comes in the form of images which suffer
information loss inherent in the techniques used to prepare and measure the system. This is …

The order parameter for a continuous transition shows diverging fluctuation near the critical
point. Here we show, through numerical simulations and scaling arguments, that the …

Engineered quantum systems can help us learn more about fundamental physics topics and
quantum technologies with real-world applications. However, building them could involve …

In the field of condensed matter physics, machine learning methods have become an
increas-ingly important instrument for researching phase transitions. Here we present a …