Speaker
Mehran Khosrojerdi
Description
This present introduces a novel approach to classifying quantum phases of matter through the use of tensor networks within a quantum machine learning (QML) framework. Beginning with an overview of QML principles and their transformative impact on condensed matter physics, I will outline the role of tensor networks in modeling and analyzing many-body systems. By applying these models to tackle phase classification problems, we demonstrate how tensor networks enhance the understanding of intricate quantum behaviors. In particular, I will present results from the ANNNI and Haldane chain models, which illustrate the efficacy of tensor networks in accurately identifying diverse phases, even in complex, strongly correlated quantum systems.
| Sessione | Quantum Machine Learning |
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