Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

The optimal control of open quantum systems is a challenging task but has a key role in
improving existing quantum information processing technologies. We introduce a general …

We characterise the measurement sensitivity, quantified by the quantum Fisher information
(QFI), of a single-fermionic thermometric probe strongly coupled to the sample of interest, a …

Quantum thermodynamic relationships in emerging nanodevices are significant but often
complex to deal with. The application of machine learning in quantum thermodynamics has …

We extend collisional quantum thermometry schemes to allow for stochasticity in the waiting
time between successive collisions. We establish that introducing randomness through a …

Using a two-level moving probe, we address the temperature estimation of a static thermal
bath modeled by a massless scalar field prepared in a thermal state. Different couplings of …

Thermometry is a fundamental parameter estimation problem that is crucial for the
advancement of natural sciences. One widely adopted approach to address this issue is the …

The heat capacity C of a given probe is a fundamental quantity that determines, among other
properties, the maximum precision in temperature estimation. In turn, C is limited by a …

We characterise the measurement sensitivity, quantified by the quantum Fisher information
(QFI), of a single-fermionic thermometric probe strongly coupled to the sample of interest, a …

In this thesis we deal with two different topics. In the first half we investigate how the
Bayesian formalism can be introduced into the problem of quantum thermometry--a field …