Quantum Phases of Time-Reversal Invariant Bose-Einstein Condensates

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dc.contributor.advisorZhang, Chuanwei
dc.creatorMaisberger, Matthew C
dc.date.accessioned2021-02-04T14:53:33Z
dc.date.available2021-02-04T14:53:33Z
dc.date.created2020-12
dc.date.issued2020-11-17
dc.date.submittedDecember 2020
dc.date.updated2021-02-04T14:53:33Z
dc.description.abstractRecent experimental realization of spin-orbit coupling (SOC) for ultracold atomic gases with the use of synthetic gauge fields provides a powerful platform for the study of novel quantum phenomena and the simulation of exotic condensed matter phases. However, in conventional schemes of SOC in ultracold bosonic gases, time-reversal symmetry, which plays a critical role in topologically nontrivial states, is broken by an effective transverse Zeeman field. We study the quantum phases of SOC Bose-Einstein condensates (BECs) with the use of a Hermite-Gaussian (HG) beam to induce Raman transitions. This treatment allows for SOC in bilayer BECs with inter-layer tunneling where time-reversal symmetry is preserved. New ground-state phases are introduced that are not seen in conventional SOC BECs. We propose a experimentally feasible setup and discuss the physical parameters under which time-reversal symmetry can be preserved. The Hamiltonians for SOC BECs are often nonlinear and the methods used for calculating the ground-state wavefunctions are computationally expensive. The wavefunctions need to be calculated on an individual basis to study the ground-state quantum phases on a granular level. We propose the use of convolutional-neural-networks (CNN) to train SOC BEC systems and reduce the computational cost of these ground-state calculations. We show the overall network setup and discuss the ranges over which the model is realizable. The proposed CNN uses a reverse-flow algorithm that allows for complex phases of the wavefunction and thus permits for a broader study of SOC BEC systems. In summary, this dissertation details how time-reversal symmetry can be preserved in SOC BECs and how predictive analytics can be used to further understand the ground-state properties of these systems
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/9168
dc.language.isoen
dc.subjectQuantum systems
dc.subjectBose-Einstein gas
dc.subjectMachine learning
dc.subjectGauge fields (Physics)
dc.subjectNeural networks (Computer science)
dc.titleQuantum Phases of Time-Reversal Invariant Bose-Einstein Condensates
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentPhysics
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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