Entropy measures quantify the amount of information and correlation present in a quantum
system. In practice, when the quantum state is unknown and only copies thereof are …

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 …

Learning problems involve settings in which an algorithm has to make decisions based on
data, and possibly side information such as expert knowledge. This study has two main …

The performance of a quantum information processing protocol is ultimately judged by
distinguishability measures that quantify how distinguishable the actual result of the protocol …

We introduce a model-independent method for the efficient simulation of low-entropy
systems, whose dynamics can be accurately described with a limited number of states. Our …

We present the variational separability verifier (VSV), which is a variational quantum
algorithm that determines the closest separable state (CSS) of an arbitrary quantum state …

Symmetry is a unifying concept in physics. In quantum information and beyond, it is known
that quantum states possessing symmetry are not useful for certain information-processing …

Generalization is the ability of quantum machine learning models to make accurate
predictions on new data by learning from training data. Here, we introduce the data quantum …

Symmetry is an important and unifying notion in many areas of physics. In quantum
mechanics, it is possible to eliminate degrees of freedom from a system by leveraging …

This study explores the dynamics of atomic mixedness and phase space quasi-probability
information of one of two moving atoms trapped in a cavity filled by a coherent field. The …