WELCOME TO THE

QUANTUM THEORY GROUP


HIGHLIGHTS


Tommaso Faorlin (Master student, UniPd) - Quantum random numbers characterization via quantum-inspired machine learning

The generation of good random numbers impacts basic research and applications beyond pure academic interests: random numbers are required for countless applications, such as cryptography and simulations. For most applications, it is of outmost importance to know if a set of numbers is truly random, pseudo-random or contains some residual correlations. Tensor networks are powerful data structures that spring from quantum many-body physics and are now increasingly applied to machine learning applications. This talk plans to present a possible intersection between these two fields, applying quantum-inspired machine learning to random number generation. We aim to perform and characterise novel statistical checks comparing and characterising the statistical quality of different number sets: correlated, pseudo-random, and quantum-random.



John Preskill (Richard P. Feynman Professor of Theoretical Physics, Caltech) - Quantum Computing and Fundamental Physics

This talk has three parts. In part 1, I discuss the current status and near-term prospects for quantum computing and quantum simulation. In part 2, I emphasize the opportunity to advance our understanding of quantum field theory, high energy physics, and nuclear physics using quantum simulation platforms. In part 3, I describe some recent and ongoing work developing classical and quantum algorithms for simulating high-energy scattering in quantum field theory, particularly in one spatial dimension.


Marco Trenti (Master student, UniPd) - CMS FCNC signal recognitions with Tensor Network Machine Learning Methods

One interesting problem in HEP is the analysis of Flavour Changing Neutral Current (FCNC) events as they violates the Standard Model (SM) prediction. These events are infact heavily suppressed in the SM by the GIM mechanism and are predicted only by supersimmetry theories. In this work we develop a Tensor Network Machine Learning Method targeting the efficient recognition of these rare events with respect to a large pool of possible background events. We will see that the hard part of this task lies in the strong unbalancement of the dataset and thus propose different strategies to overcome this issue.