Analyzing Quantum Phase Transitions with Modern Artificial Intelligence Architectures

16:00 - 18:00, November 9, 2022
IN PERSON at Leibniz Rechenzentrum, Boltzmannstraße 1, 85748 Garching b. München
& Online
Abstract:

In the quantum phase transitions, an accurate representation from single-shot experimental momentum-space density images of ultracold quantum gases can be one challenge task due to the noisy images which make data extraction difficult. Introducing some deep learning techniques to denoise the experimental image, mapping two-dimensional phase diagram of Haldane model has shown improvement in attaining accurate results that is however impossible to achieve with conventional methods. In this study two different machine learning models are implemented and compared to study their accuracies using theoretical results as a reference.

Hossam Ahmed

Biography:

Hossam Ahmed is currenly working at LRZ and in the meanwhile  a master student of high performance and quantum computing at DIT. He is working in the field of integration of quantum computing and high performance computing. He completed his bachelor's in electrical and electronics engineering at Middle East Technical University and had several contributions in IoT researches and meta-material design. After graduation he worked as an embedded systems engineer before joining the first wave of quantum computing.

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