Arxiv Selection Dec 2020
Dec 1- Dec 7 Bhaskar Mukherjee, Dec 8- Dec 12 Zehan Li
Dec 11
arXiv:2012.07611 [pdf, ps, other] cond-mat.quant-gas quant-ph
Fast-forward scaling of atom-molecule conversion in Bose-Einstein condensates
Authors: JingJun Zhu, Xi Chen
Robust stimulated Raman exact passages are requisite for controlling nonlinear quantum systems, with the wide applications ranging from ultracold molecules, non-linear optics to superchemistry. Inspired by shortcuts to adiabaticity, we propose the fast-forward scaling of stimulated Raman adiabatic processes with the nonlinearity involved, describing the transfer from an atomic Bose-Einstein condensate to a molecular one by controllable external fields. The fidelity and robustness of atom-molecule conversion are shown to surpass those of conventional adiabatic passages, assisted by fast-forward driving field. Finally, our results are extended to the fractional stimulated Raman adiabatic processes for the coherent superposition of atomic and molecular states.
Dec 10
arXiv:2012.05322 [pdf, other] cond-mat.mtrl-sci cs.LG
Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Images
Authors: Robbie Sadre, Colin Ophus, Anstasiia Butko, Gunther H Weber
Phase contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of 2D materials such as monolayer graphene due to its high dose efficiency. However, phase contrast imaging can produce complex nonlinear contrast, even for weakly-scattering samples. It is therefore difficult to develop fully-automated analysis routines for phase contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method to a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily-adaptable source code for all results in this paper, and discuss potential applications for deep learning in fully-automated TEM image analysis.
Dec 9
arXiv:2012.05265 [pdf, other] cond-mat.str-el
Tensor network study of the m=1/2 magnetization plateau in the Shastry-Sutherland model at finite temperature
Authors: Piotr Czarnik, Marek M. Rams, Philippe Corboz, Jacek Dziarmaga
The two-dimensional iPEPS tensor network is evolved in imaginary time with the full update (FU) algorithm to simulate the Shastry-Sutherland model in a magnetic field at finite temperature directly in the thermodynamic limit. We focus on the phase transition into the m=1/2 magnetization plateau, which was observed in experiments on SrCu2(BO3)2. For the largest simulated bond dimension, the early evolution in the high-temperature regime is simulated with the simple update (SU) scheme and then, as the correlation length increases, continued with the FU scheme towards the critical regime. We apply a small-symmetry breaking bias field and then extrapolate towards zero bias using a simple scaling theory in the bias field. The combined SU+FU scheme provides an accurate estimate of the critical temperature, even though the results could not be fully converged in the bond dimension in the vicinity of the transition. The critical temperature estimate is improved with a generalized scaling theory that combines two divergent length scales: one due to the bias and the other due to the finite bond dimension. The obtained results are consistent with the transition being in the universality class of the two-dimensional classical Ising model. The estimated critical temperature is 3.5(2)K, which is well above the temperature 2.1K used in the experiments.
Dec 8
arXiv:2012.04594 [pdf, other] cond-mat.dis-nn physics.app-ph physics.comp-ph
Nanoscale neural network using non-linear spin-wave interference
Authors: Adam Papp, Wolfgang Porod, Gyorgy Csaba
We demonstrate the design of a neural network, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.
