The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

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 …

Characterizing the interactions and dynamics of quantum mechanical systems is an
essential task in developing quantum technologies. We propose an efficient protocol based …

The physics of a closed quantum mechanical system is governed by its Hamiltonian.
However, in most practical situations, this Hamiltonian is not precisely known, and ultimately …

A quantum coherent screening cloud around a magnetic impurity in metallic systems is the
hallmark of the antiferromagnetic Kondo effect. Despite the central role of the Kondo effect in …

Determining Hamiltonian parameters from noisy experimental measurements is a key task
for the control of experimental quantum systems. An interesting experimental platform where …

Extracting Hamiltonian parameters from available experimental data is a challenge in
quantum materials. In particular, real-space spectroscopy methods such as scanning …

Reliable quantum technology requires knowledge of the dynamics governing the underlying
system. This problem of characterizing and benchmarking quantum devices or experiments …

The computation of dynamical correlators of quantum many-body systems represents an
open critical challenge in condensed matter physics. While powerful methodologies have …

We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from
time-resolved measurements of spin-spin correlators, which can be obtained via nuclear …