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相关论文: Many-body mobility edges in 1D and 2D revealed by …

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We study the many-body localization (MBL) transition in interacting fermionic systems on disordered one-dimensional lattices using a physics-informed machine-learning framework. Instead of feeding full many-body wave functions into the…

无序系统与神经网络 · 物理学 2026-03-24 Siqi Dai , Tian-Cheng Yi , Xingbo Wei , Yunbo Zhang

We employ a convolutional neural network to explore the distinct phases in random spin systems with the aim to understand the specific features that the neural network chooses to identify the phases. With the energy spectrum normalized to…

无序系统与神经网络 · 物理学 2020-07-24 Rubah Kausar , Wen-Jia Rao , Xin Wan

We experimentally observe many-body localization of interacting fermions in a one-dimensional quasi-random optical lattice. We identify the many-body localization transition through the relaxation dynamics of an initially-prepared charge…

We construct a solvable spin chain model of many-body localization (MBL) with a tunable mobility edge. This simple model not only demonstrates analytically the existence of mobility edges in interacting one-dimensional (1D) disordered…

统计力学 · 物理学 2015-07-07 Yichen Huang

While many studies point towards the existence of many-body localization (MBL) in one dimension, the fate of higher-dimensional strongly disordered systems is a topic of current debate. The latest experiments as well as several recent…

无序系统与神经网络 · 物理学 2024-05-13 Joey Li , Amos Chan , Thorsten B. Wahl

We study the many-body localization aspects of single-particle mobility edges in fermionic systems. We investigate incommensurate lattices and random disorder Anderson models. Many-body localization and quantum nonergodic properties are…

统计力学 · 物理学 2016-06-07 Xiaopeng Li , J. H. Pixley , Dong-Ling Deng , Sriram Ganeshan , S. Das Sarma

Achieving robust multi-person 2D body landmark localization and pose estimation is essential for human behavior and interaction understanding as encountered for instance in HRI settings. Accurate methods have been proposed recently, but…

计算机视觉与模式识别 · 计算机科学 2019-12-03 Angel Martínez-González , Michael Villamizar , Olivier Canévet , Jean-Marc Odobez

We introduce novel characterizations for many-body phase transitions between delocalized and localized phases based on the system's sensitivity to boundary conditions. In particular, we change boundary conditions from periodic to…

强关联电子 · 物理学 2021-01-27 Mohammad Pouranvari , Shiuan-Fan Liou

The many-body localization (MBL) transition is a quantum phase transition involving highly excited eigenstates of a disordered quantum many-body Hamiltonian, which evolve from "extended/ergodic" (exhibiting extensive entanglement entropies…

无序系统与神经网络 · 物理学 2018-04-20 Piero Naldesi , Elisa Ercolessi , Tommaso Roscilde

Determining phase diagrams and phase transitions semi-automatically using machine learning has received a lot of attention recently, with results in good agreement with more conventional approaches in most cases. When it comes to more…

无序系统与神经网络 · 物理学 2019-12-04 Hugo Théveniaut , Fabien Alet

In this paper, a mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting the mode coefficients. Four-fold degenerated $LP_{11}$ series has…

计算机视觉与模式识别 · 计算机科学 2022-07-13 Hyuntai Kim

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to…

As disorder strength increases in quantum many-body systems a new phase of matter, the so-called anybody localization, emerges across the whole spectrum. This transition is energy dependent, a phenomenon known as mobility edge, such that…

无序系统与神经网络 · 物理学 2023-02-02 Rozhin Yousefjani , Abolfazl Bayat

We investigate many-body localization of interacting spinless fermions in a one-dimensional disordered and tilted lattice. The fermions undergo energy-dependent transitions from ergodic to Stark many-body localization driven by the tilted…

量子物理 · 物理学 2021-03-03 Li Zhang , Yongguan Ke , Wenjie Liu , Chaohong Lee

We study the transitions between ergodic and many-body localized phases in spin systems, subject to quenched disorder, including the Heisenberg chain and the central spin model. In both cases systems with common spin lengths $1/2$ and $1$…

无序系统与神经网络 · 物理学 2021-05-19 John Schliemann , Joao Vitor I. Costa , Paul Wenk , J. Carlos Egues

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime.…

无序系统与神经网络 · 物理学 2017-07-04 Frank Schindler , Nicolas Regnault , Titus Neupert

In one dimension, noninteracting particles can undergo a localization-delocalization transition in a quasiperiodic potential. Recent studies have suggested that this transition transforms into a many-body localization (MBL) transition upon…

无序系统与神经网络 · 物理学 2015-12-09 Ranjan Modak , Subroto Mukerjee

Within one-dimensional disordered models of interacting fermions we perform a numerical study of several dynamical density correlations, which can serve as hallmarks of the transition to the many-body localized state. Results confirm that…

强关联电子 · 物理学 2016-12-30 M. Mierzejewski , J. Herbrych , P. Prelovšek

Chains of superconducting circuit devices provide a natural platform for studies of synthetic bosonic quantum matter. Motivated by the recent experimental progress in realizing disordered and interacting chains of superconducting transmon…

量子物理 · 物理学 2019-10-09 Tuure Orell , Alexios A. Michailidis , Maksym Serbyn , Matti Silveri

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image…

计算机视觉与模式识别 · 计算机科学 2016-02-11 Hoo-Chang Shin , Holger R. Roth , Mingchen Gao , Le Lu , Ziyue Xu , Isabella Nogues , Jianhua Yao , Daniel Mollura , Ronald M. Summers
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