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SUMMARY:Unsupervised Machine learning approaches in quantum dynamics
DTSTART:20260602T053000Z
DTEND:20260602T063000Z
DTSTAMP:20260618T153900Z
UID:indico-event-9355@scitalks.tifr.res.in
DESCRIPTION:Speakers: Sambuddha Sanyal (IISER Tirupati)\n\nWe develop a no
 vel\, fully unsupervised learning framework\, consisting of a  Convolutio
 nal autoencoder and Quantum clustering equipped with a unified evaluation 
 score encapsulating separability and topological stability\, to identify d
 ifferent phases and precisely locate the critical or crossover regime of q
 uantum phase transitions using the pixilated snapshots of density profile 
 evolution. We further illustrate\, for the first time\, the application of
  our machine learning framework in clustering distinct temporal regimes\, 
 which are characterized by critical dynamics such as sub-diffusive\, super
 -diffusive\, and diffusive dynamics. We employ transfer learning\, and qua
 litatively show the finite size scaling of crossover region. The results d
 emonstrate that our framework is robust to highly imbalanced heterogeneous
  data and multiple transitions.\nIn this work\, we have examined both sing
 le-particle and many-body systems. In single particle system\, we investig
 ated quasi-periodic Hamiltonians specifically Aubry-Andre\, Generalized Au
 bry-Andre\, Rice-Mele models\, as well as the true disorder Hamiltonian\, 
 namely the Anderson model. In the many-body system\, we studied Bose-Hubba
 rd model to capture Superfluid-Mott insulator transition and the 1D Fermi-
 Hubbard with quasi-periodic as well as true disorder onsite potential to c
 apture the Ergodic-Many body localization (MBL) transition.\n\nhttps://sci
 talks.tifr.res.in/event/9355/
LOCATION:AG 69 and on zoom
URL:https://scitalks.tifr.res.in/event/9355/
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