The prospect of using the quantum nature of light for long distance quantum communication keeps spurring the search and investigation of suitable sources of entangled photons. Semiconductor quantum dots (QDs), also dubbed “artificial atoms”, are arguably one of the most attractive, as they can generate pairs of polarization-entangled photons with high efficiency and with near-unity degree of entanglement. Despite recent advances, however, the exploitation of photons from QDs in advanced quantum communication protocols remains a major open
challenge.
In this talk, it will be discussed how photons generated by a GaAs quantum dot canbe used to implement quantum teleportation and entanglement swapping protocols with fidelities above the classical limit. Moreover, the first steps towards the construction of a quantum-dot based quantum network for secure communicationwithin the campus of Sapienza University of Rome will be presented. A discussion on future challenges and perspectives will conclude the talk.

The ordinary nuclear matter at high temperature (T> 10¹²K) should undergo a transition to a plasma of quark and gluons, as now predicted by Quantum ChromoDynamics (QCD) at finite temperature. This state of matter that have permeated the first microseconds of the Early Universe can be created for transient of time (10^-22s) in ultra-relativistic heavy-ion collision at RHIC and LHC colliders.

I will overview some basic scales and degrees of freedom of this research area and review some main aspects of this research area emphasizing how over the years it has become possible to “see” inside the created matter probing its substructures at finer and finer scale. In the second part, I will focus the dynamics of the heavy quark in the QGP showing how the first results has shown a strong non-perturbative interaction inducing transport properties in agreement with the expectation from lattice QCD. I will conclude discussing the emerging studies on the impact of the huge initial electromagnetic fields (10¹⁸ Gauss) on their dynamics.

Artificial Intelligence (AI) has been implemented in the field of Medical Imaging since the eighties. The great emphasis in recent years on Deep Learning methods is triggering a new revolution in this field, potentially leading to improve disease diagnosis and patient prognosis.

Some specific challenges related to methodological aspects need to be addressed before the AI potential can be fully exploited in Medical Imaging applications. While AI models require large amounts of labelled data during the training phase, it is very difficult to collect sufficiently large samples of properly annotated medical images. Thus, efficient and robust strategies to learn from limited examples have to be set up.

An additional crucial challenge to face in medical applications of AI is to make these solutions understandable by humans. Dedicated Explainable AI (XAI) solutions are urgently needed in the field of medical applications to make clinicians and patients trust AI-based solutions and to enable their adoption in clinical workflows.