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DTSTART:20241211T090000Z
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LOCATION:ICFO Auditorium and Online (Teams)
SUMMARY:ICFO | ADRIANO MACARONE PALMIERI
CLASS:PUBLIC
DESCRIPTION:The thesis explores the application of supervised deep learning
  (DL) to mitigate noise in quantum state estimation protocols\, to offer a
  viable tool for quantum technologies development\, that leverages quantum
  properties\, like entanglement. This is vital for quantum information pro
 cessing and is used in applications like quantum teleportation\, quantum k
 ey distribution\, and superdense coding. However\, the practical implement
 ation of these technologies is challenged by noise and errors\, making acc
 urate certification of quantum states essential.\nTraditionally\, state to
 mography is the best possible desiderata\, but it is resource-intensive. A
 lternative methods with better scaling\, such as permutationally invariant
  states and shadows\, have been proposed\, though they are limited in scop
 e\, because limited to specific classes of states or can estimate some qua
 ntum properties only. The thesis specifically investigates whether supervi
 sed DL can be used to mitigate noise and achieve full quantum state estima
 tion under various conditions\, including limited resources\, different no
 isy sources\, and\, last\, incomplete information.\nThe research introduce
 s a novel approach using the out-of-distribution paradigm to extend the ap
 plicability of supervised deep learning to unknown data distributions\, su
 ch as noisy quantum states measured with imperfect setups. This study at a
  higher depth the generalization ability of deep learning protocols while 
 maintaining the simplicity of trained supervised neural networks. In this 
 way\, seamless application from synthetic to experimental data is allowed.
  At the same time\, the computational aspect involves analyzing the comple
 xity of different models and their learning abilities\, and noise mitigati
 on capabilities\, and showcasing transformer-based models in certifying ge
 nuine k-body entanglement as superior.\nLastly\, the thesis addresses nois
 e characterization using deep learning\, particularly how this can infer e
 nvironmental noise parameters from a single-qubit probe without fixed-time
  conditions. This contributes to better noise reduction and system control
  in quantum technologies.\n&nbsp\;\nWednesday December 11\, 10:00 h. ICFO 
 Auditorium and online via Teams\nThesis Director: Prof. Dr. Maciej Lewenst
 ein
DTSTAMP:20260407T055228Z
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