Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …

Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

The operator entanglement (OE) is a key quantifier of the complexity of a reduced density
matrix. In out-of-equilibrium situations, eg, after a quantum quench of a product state, it is …

Measurements with randomly chosen settings determine many important properties of
quantum states without the need for a shared reference frame or calibration. They naturally …

We investigate a separability criterion based on the computable cross-norm (CCNR), and a
related quantity called the CCNR negativity. We introduce a reflected version of the CCNR …

The concept of randomized measurements on individual particles has proven to be useful
for analyzing quantum systems and is central for methods like shadow tomography of …

We calculate the amount of entanglement shared by two intervals in the ground state of a
(1+ 1)-dimensional conformal field theory (CFT), quantified by an entanglement measure E …

Estimating nonlinear functions of quantum states, such as the moment tr (ρ m), is of
fundamental and practical interest in quantum science and technology. Here we show a …

We present a technique for enhancing the estimation of quantum state properties by
incorporating approximate prior knowledge about the quantum state. This consists in …

Analog quantum simulation is an essential routine for quantum computing and plays a
crucial role in studying quantum many-body physics. Typically, the quantum evolution of an …