Causal inference from experimental observations turns out to be of primary importance in several scientific fields. Causality theory has, in fact, become a fundamental tool for a wide range of applications, such as statistics and machine learning, and, through these two applications, also in genetics, social studies and economics.
Causality theory provides a powerful new tool for addressing quantum information problems. Recently, it has been shown that quantum causality allows the development of new protocols based on fewer constraints, unlike those previously implemented.
Relevant examples are the certified generation of purely random numbers [Agre2020], or the production of quantum effects within a network between different laboratories [Pode2020]. This line of research aims to take these activities to the next level, achieving both theoretical and experimental breakthroughs on the foundations and experimental implications of quantum causality.
Specifically, the following goals will be pursued:
1) Analyze the emergence of new types of non-classical behavior in different types of causal networks that have never been considered before. This includes the use of long-distance links via optical fiber and/or free-space between two or more nodes.
2) Develop new quantum information protocols for quantum networks formed by multiple participants.
3) Exploit Machine Learning techniques for the analysis of data obtained through complex experimental structures.
The Enrico Fermi Research Center is also dedicated to the development of this new area of research.