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Andrew J Millis

Publications and source records attributed to Andrew J Millis.

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Topological Kondo Insulator from Spin Loop Currents

We demonstrate that interacting electrons in AB-stacked $\mathrm{MoTe}_2/\mathrm{WSe}_2$ realize a topological Kondo insulator at hole filling $ν=2$ per moiré unit cell. In the presence of only local correlations, a symmetry of the moiré-scale bandstructure enforces a compensated topological semimetal by tying band inversion to band overlap. We show that non-local interactions change the physics qualitatively, since they allow intrinsic, quantum-geometry-induced spin loop currents to feed back on the effective bandstructure, which lift the remaining accidental degeneracies and open a full gap in the spectrum, leading to a fully gapped topological Kondo insulator. We establish this using real-frequency dynamical mean-field theory to capture Kondo physics alongside Hartree-Fock for non-local interactions. The topological Kondo insulator emerges at intermediate displacement fields, where strong correlations manifest through an enhanced spin susceptibility, a suppressed charge susceptibility, and a stronger thermal dependence of the resistivity. Our results are in good agreement with recent experiments on $\mathrm{MoTe}_2/\mathrm{WSe}_2$ bilayers demonstrating topological to trivial phase transitions controlled by the displacement field.

cond-mat.str-el

Machine Learning Phase Field Reconstruction in a Bose-Einstein Condensate

A basic challenge in experimental physics is the extraction of information related to variables that are not directly measured. The challenge is particularly severe in quantum systems where one may be interested in correlations of operators that are not diagonal in the measurement basis. In this paper we take a step towards addressing this issue in the context of Boson superfluids, where standard in-situ imaging yields only the spatially resolved density, leaving the phase field - crucial for identifying topological defects such as vortices and confirming superfluidity - indirectly encoded. Previous work has shown that the location of vortices in the phase field may be detected, but has not solved the problems of fully reconstructing the phase or identifying the charge (vortex vs. antivortex). This paper shows that a combination of a deep machine learning (ML) model and classical computer vision post-processing steps can address this gap. We use realistic snapshots of the thermal state of a two-dimensional BEC in a harmonic trap using synthetic data obtained from projected Gross-Pitaevskii equation simulations to train a U-Net-based architecture to infer the absolute values of the phase field gradients from an observed density field, and then employ a separate ML model to locate the positions of the vortex cores and a post-processing graphical analysis to determine with high accuracy the phase field, including the quantized charge of each vortex.

cond-mat.quant-gas