arXiv - PHYS - Disordered Systems and Neural Networks最新文献_第8页

Disorder free many-body localization transition in two quasiperiodically coupled Heisenberg spin chains 两个准周期耦合海森堡自旋链中的无序多体局域化转变

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-05-07 DOI: arxiv-2405.04516

K. G. S. H. Gunawardana, Bruno Uchoa

{"title":"Disorder free many-body localization transition in two quasiperiodically coupled Heisenberg spin chains","authors":"K. G. S. H. Gunawardana, Bruno Uchoa","doi":"arxiv-2405.04516","DOIUrl":"https://doi.org/arxiv-2405.04516","url":null,"abstract":"Disorder free many-body localization (MBL) can occur in interacting systems\u0000that can dynamically generate their own disorder. We address the thermal-MBL\u0000phase transition of two isotropic Heisenberg spin chains that are\u0000quasi-periodically coupled to each other. The spin chains are incommensurate\u0000and are coupled through a short range exchange interaction of the $XXZ$ type\u0000that decays exponentially with the distance. Using exact diagonalization,\u0000matrix product states and density matrix renormalization group, we calculate\u0000the time evolution of the entanglement entropy at long times and extract the\u0000inverse participation ratio in the thermodynamic limit. We show that this\u0000system has a robust MBL phase. We establish the phase diagram with the onset of\u0000MBL as a function of the interchain exchange coupling and of the\u0000incommensuration between the spin chains. The Ising limit of the interchain\u0000interaction optimizes the stability of the MBL phase over a broad range of\u0000incommensurations above a given critical exchange coupling. Incorporation of\u0000interchain spin flips significantly enhances entanglement between the spin\u0000chains and produces delocalization, favoring a pre-thermal phase whose\u0000entanglement entropy grows logarithmically with time.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Platform Autonomous Control of Minimal Kitaev Chains 最小基塔耶夫链的跨平台自主控制

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-05-07 DOI: arxiv-2405.04596

David van Driel, Rouven Koch, Vincent P. M. Sietses, Sebastiaan L. D. ten Haaf, Chun-Xiao Liu, Francesco Zatelli, Bart Roovers, Alberto Bordin, Nick van Loo, Guanzhong Wang, Jan Cornelis Wolff, Grzegorz P. Mazur, Tom Dvir, Ivan Kulesh, Qingzhen Wang, A. Mert Bozkurt, Sasa Gazibegovic, Ghada Badawy, Erik P. A. M. Bakkers, Michael Wimmer, Srijit Goswami, Jose L. Lado, Leo P. Kouwenhoven, Eliska Greplova

{"title":"Cross-Platform Autonomous Control of Minimal Kitaev Chains","authors":"David van Driel, Rouven Koch, Vincent P. M. Sietses, Sebastiaan L. D. ten Haaf, Chun-Xiao Liu, Francesco Zatelli, Bart Roovers, Alberto Bordin, Nick van Loo, Guanzhong Wang, Jan Cornelis Wolff, Grzegorz P. Mazur, Tom Dvir, Ivan Kulesh, Qingzhen Wang, A. Mert Bozkurt, Sasa Gazibegovic, Ghada Badawy, Erik P. A. M. Bakkers, Michael Wimmer, Srijit Goswami, Jose L. Lado, Leo P. Kouwenhoven, Eliska Greplova","doi":"arxiv-2405.04596","DOIUrl":"https://doi.org/arxiv-2405.04596","url":null,"abstract":"Contemporary quantum devices are reaching new limits in size and complexity,\u0000allowing for the experimental exploration of emergent quantum modes. However,\u0000this increased complexity introduces significant challenges in device tuning\u0000and control. Here, we demonstrate autonomous tuning of emergent Majorana zero\u0000modes in a minimal realization of a Kitaev chain. We achieve this task using\u0000cross-platform transfer learning. First, we train a tuning model on a theory\u0000model. Next, we retrain it using a Kitaev chain realization in a\u0000two-dimensional electron gas. Finally, we apply this model to tune a Kitaev\u0000chain realized in quantum dots coupled through a semiconductor-superconductor\u0000section in a one-dimensional nanowire. Utilizing a convolutional neural\u0000network, we predict the tunneling and Cooper pair splitting rates from\u0000differential conductance measurements, employing these predictions to adjust\u0000the electrochemical potential to a Majorana sweet spot. The algorithm\u0000successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV\u0000in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a\u0000sweet spot in 45 minutes or less. This advancement is a stepping stone towards\u0000autonomous tuning of emergent modes in interacting systems, and towards\u0000foundational tuning machine learning models that can be deployed across a range\u0000of experimental platforms.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quenches in the Sherrington-Kirkpatrick model 谢林顿-柯克帕特里克模型中的淬火现象

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-05-07 DOI: arxiv-2405.04267

Vittorio Erba, Freya Behrens, Florent Krzakala, Lenka Zdeborová

{"title":"Quenches in the Sherrington-Kirkpatrick model","authors":"Vittorio Erba, Freya Behrens, Florent Krzakala, Lenka Zdeborová","doi":"arxiv-2405.04267","DOIUrl":"https://doi.org/arxiv-2405.04267","url":null,"abstract":"The Sherrington-Kirkpatrick (SK) model is a prototype of a complex non-convex\u0000energy landscape. Dynamical processes evolving on such landscapes and locally\u0000aiming to reach minima are generally poorly understood. Here, we study\u0000quenches, i.e. dynamics that locally aim to decrease energy. We analyse the\u0000energy at convergence for two distinct algorithmic classes, single-spin flip\u0000and synchronous dynamics, focusing on greedy and reluctant strategies. We\u0000provide precise numerical analysis of the finite size effects and conclude\u0000that, perhaps counter-intuitively, the reluctant algorithm is compatible with\u0000converging to the ground state energy density, while the greedy strategy is\u0000not. Inspired by the single-spin reluctant and greedy algorithms, we\u0000investigate two synchronous time algorithms, the sync-greedy and sync-reluctant\u0000algorithms. These synchronous processes can be analysed using dynamical mean\u0000field theory (DMFT), and a new backtracking version of DMFT. Notably, this is\u0000the first time the backtracking DMFT is applied to study dynamical convergence\u0000properties in fully connected disordered models. The analysis suggests that the\u0000sync-greedy algorithm can also achieve energies compatible with the ground\u0000state, and that it undergoes a dynamical phase transition.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A method for quantifying the generalization capabilities of generative models for solving Ising models 量化求解伊辛模型的生成模型泛化能力的方法

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-05-06 DOI: arxiv-2405.03435

Qunlong Ma, Zhi Ma, Ming Gao

{"title":"A method for quantifying the generalization capabilities of generative models for solving Ising models","authors":"Qunlong Ma, Zhi Ma, Ming Gao","doi":"arxiv-2405.03435","DOIUrl":"https://doi.org/arxiv-2405.03435","url":null,"abstract":"For Ising models with complex energy landscapes, whether the ground state can\u0000be found by neural networks depends heavily on the Hamming distance between the\u0000training datasets and the ground state. Despite the fact that various recently\u0000proposed generative models have shown good performance in solving Ising models,\u0000there is no adequate discussion on how to quantify their generalization\u0000capabilities. Here we design a Hamming distance regularizer in the framework of\u0000a class of generative models, variational autoregressive networks (VAN), to\u0000quantify the generalization capabilities of various network architectures\u0000combined with VAN. The regularizer can control the size of the overlaps between\u0000the ground state and the training datasets generated by networks, which,\u0000together with the success rates of finding the ground state, form a\u0000quantitative metric to quantify their generalization capabilities. We conduct\u0000numerical experiments on several prototypical network architectures combined\u0000with VAN, including feed-forward neural networks, recurrent neural networks,\u0000and graph neural networks, to quantify their generalization capabilities when\u0000solving Ising models. Moreover, considering the fact that the quantification of\u0000the generalization capabilities of networks on small-scale problems can be used\u0000to predict their relative performance on large-scale problems, our method is of\u0000great significance for assisting in the Neural Architecture Search field of\u0000searching for the optimal network architectures when solving large-scale Ising\u0000models.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emergent Non-Abelian Thouless Pumping Induced by the Quasiperiodic Disorder 由准周期性紊乱诱发的新兴非阿贝尔无汝泵送

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-29 DOI: arxiv-2404.18491

Sen Huang, Yan-Qing Zhu, Zhi Li

{"title":"Emergent Non-Abelian Thouless Pumping Induced by the Quasiperiodic Disorder","authors":"Sen Huang, Yan-Qing Zhu, Zhi Li","doi":"arxiv-2404.18491","DOIUrl":"https://doi.org/arxiv-2404.18491","url":null,"abstract":"We investigate the non-Abelian Thouless pumping in a disorder tunable Lieb\u0000chain with degenerate flat bands. The results reveal that quasiperiodic\u0000disorder will cause a topological phase transition from the trivial (without\u0000non-Abelian Thouless pumping) to the non-trivial (with non-Abelian Thouless\u0000pumping) phase. The mechanism behind is that the monopole originally outside\u0000the topological region can be driven into the topological region due to the\u0000introduction of quasiperiodic disorder. Moreover, since the corresponding\u0000monopole will turn into a nodal line to spread beyond the boundaries of the\u0000topological region, the system with large disorder strength will result in the\u0000disappearance of non-Abelian Thouless pumping. Furthermore, we numerically\u0000simulate the Thouless pumping of non-Abelian systems, and the evolution results\u0000of center of mass' displacement are consistent with the Chern number. Finally,\u0000we discuss the localization properties of the system and find that, similar to\u0000[PRL 130, 206401(2023)], the inverse Anderson transition does not occur in the\u0000system with the increase of quasiperiodic strength, while the system still\u0000maintains the coexistence of localized and extended states.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network-theory based modeling of avalanche dynamics in percolative tunnelling networks 基于网络理论的渗滤隧道网络雪崩动力学建模

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-29 DOI: arxiv-2404.18600

Vivek Dey, Steffen Kampman, Rafael Gutierrez, Gianaurelio Cuniberti, Pavan Nukala

{"title":"Network-theory based modeling of avalanche dynamics in percolative tunnelling networks","authors":"Vivek Dey, Steffen Kampman, Rafael Gutierrez, Gianaurelio Cuniberti, Pavan Nukala","doi":"arxiv-2404.18600","DOIUrl":"https://doi.org/arxiv-2404.18600","url":null,"abstract":"Brain-like self-assembled networks can infer and analyze information out of\u0000unorganized noisy signals with minimal power consumption. These networks are\u0000characterized by spatiotemporal avalanches and their crackling behavior, and\u0000their physical models are expected to predict and understand their\u0000computational capabilities. Here, we use a network theory-based approach to\u0000provide a physical model for percolative tunnelling networks, found in Ag-hBN\u0000system, consisting of nodes (atomic clusters) of Ag intercalated in the hBN van\u0000der Waals layers. By modeling a single edge plasticity through constitutive\u0000electrochemical filament formation, and annihilation through Joule heating, we\u0000identify independent parameters that determine the network connectivity. We\u0000construct a phase diagram and show that a small region of the parameter space\u0000contains signals which are long-range temporally correlated, and only a subset\u0000of them contains crackling avalanche dynamics. Physical systems spontaneously\u0000selforganize to this region for possibly maximizing the efficiency of\u0000information transfer.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast relaxation in metallic glasses studied by measurements of the internal friction at high frequencies 通过测量高频内摩擦力研究金属玻璃的快速弛豫

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-27 DOI: arxiv-2404.17948

G. V. Afonin, J. C. Qiao, A. S. Makarov, N. P. Kobelev, V. A. Khonik

引用次数: 0

A tale of two localizations: coexistence of flat bands and Anderson localization in a photonics-inspired amorphous system 两种局域化的故事：光子学启发的非晶系统中平带和安德森局域化的共存

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-26 DOI: arxiv-2404.17578

Elizabeth J. Dresselhaus, Alexander Avdoshkin, Zhetao Jia, Matteo Secli, Boubacar Kante, Joel E. Moore

{"title":"A tale of two localizations: coexistence of flat bands and Anderson localization in a photonics-inspired amorphous system","authors":"Elizabeth J. Dresselhaus, Alexander Avdoshkin, Zhetao Jia, Matteo Secli, Boubacar Kante, Joel E. Moore","doi":"arxiv-2404.17578","DOIUrl":"https://doi.org/arxiv-2404.17578","url":null,"abstract":"Emerging experimental platforms use amorphousness, a constrained form of\u0000disorder, to tailor meta-material properties. We study localization under this\u0000type of disorder in a class of $2D$ models generalizing recent experiments on\u0000photonic systems. We explore two kinds of localization that emerge in these\u0000models: Anderson localization by disorder, and the existence of compact,\u0000macroscopically degenerate localized states as in many crystalline flat bands.\u0000We find localization properties to depend on the symmetry class within a family\u0000of amorphized kagom'{e} tight-binding models, set by a tunable synthetic\u0000magnetic field. The flat-band-like degeneracy innate to kagom'{e} lattices\u0000survives under amorphousness without on-site disorder. This phenomenon arises\u0000from the cooperation between the structure of the compact localized states and\u0000the geometry of the amorphous graph. For particular values of the field, such\u0000states emerge in the amorphous system that were not present on the kagom'{e}\u0000lattice in the same field. For generic states, the standard paradigm of\u0000Anderson localization is found to apply as expected for systems with\u0000particle-hole symmetry (class D), while a similar interpretation does not\u0000extend to our results in the general unitary case (class A). The structure of\u0000amorphous graphs, which arise in current photonics experiments, allows exact\u0000statements about flat-band-like states, including such states that only exist\u0000in amorphous systems, and demonstrates how the qualitative behavior of a\u0000disordered system can be tuned at fixed graph topology.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140809864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fidelity and criticality in the nonreciprocal Aubry-Andr{é}-Harper model 非互惠奥布里-安德烈-哈珀模型的保真度和临界度

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-25 DOI: arxiv-2404.16704

Chen-Chang Zeng, Zhen Cai, Guang-Heng Wang, Gaoyong Sun

引用次数: 0

Storage Capacity Evaluation of the Quantum Perceptron using the Replica Method 使用复制法评估量子感知器的存储容量

arXiv - PHYS - Disordered Systems and Neural Networks Pub Date : 2024-04-23 DOI: arxiv-2404.14785

Mitsuru Urushibata, Masayuki Ohzeki

引用次数: 0