Machine learning using quantum light
Quantum photonic technologies have achieved considerable success across various disciplines, including quantum computing, quantum communications, and quantum metrology. Conversely, the ability to discern patterns within voluminous data sets is highly sought after, with Machine Learning (ML) having emerged as a powerful solution to address this challenge. The processing speed of photonic hardware, in conjunction with the potential for computation enhancement resulting from quantum properties, positions quantum photonics as a promising platform for the implementation of ML algorithms. In this thesis, we focus on reservoir computing (RC) as an ML framework that is suitable for time series processing. It provides fast and energy-efficient training in comparison to more conventional methods and is also appropriate for physical implementations. In this thesis, we explore the possibilities brought by quantum photonic setups in the field of quantum RC. Furthermore, we leverage the capabilities of state-of-the-art quantum photonic hardware to tackle key challenges in quantum reservoir computing, as well as expand the potential of quantum machine learning.
Supervisors:
MC Soriano, GL Giorgi, R Zambrini
Jury:
President: Dr. Gerardo Adesso (Nottingham Univ.)
Vocal: Dra. Valeria Cimini (Univ. La Sapienza, Roma)
Secretary: Dr. Jan Sperling (Paderborn Univ.)
Zoom link: https://us06web.zoom.us/meetings/82714480173/invitations?signature=6LeY4A5JZcs_kdIGGUEceSrWoDqiLZNYBivYCGWq3bo
Detalls de contacte:
Gian Luca Giorgi Contact form