Speaker
Tanmoy Bhattacharya
(Los Alamos National Laboratory)
Description
Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states. In this presentation we develop QDL techniques for detecting the phase transition in the 2+1-dimensional toric-code loop-gas model in a magnetic field. Our unsupervised QDL approach recovers the phase structure and locates the phase transition with a small offset as expected in finite volumes; both supervised and unsupervised QDL methods outperform classical alternatives. These findings establish QDL as an effective framework for characterizing topological quantum matter, studying finite volume effects, and probing phase diagrams.
Author
Tanmoy Bhattacharya
(Los Alamos National Laboratory)
Co-authors
Dr
Shamminuj Aktar
(Los Alamos National Laboratory)
Dr
Andreas Bärtschi
(Los Alamos National Laboratory)
Dr
Rishabh Bharadwaj
(Los Alamos National Laboratory)
Dr
Stephan Eidenbenz
(Los Alamos National Laboratory)