arXiv: Disordered Systems and Neural Networks最新文献

Multifractality and Fock-space localization in many-body localized states: One-particle density matrix perspective 多体定域态中的多重分形和fock空间定域:单粒子密度矩阵视角

arXiv: Disordered Systems and Neural Networks Pub Date : 2021-04-19 DOI: 10.1103/PhysRevB.103.214206

Takahiro Orito, K. Imura

{"title":"Multifractality and Fock-space localization in many-body localized states: One-particle density matrix perspective","authors":"Takahiro Orito, K. Imura","doi":"10.1103/PhysRevB.103.214206","DOIUrl":"https://doi.org/10.1103/PhysRevB.103.214206","url":null,"abstract":"Many-body localization (MBL) is well characterized in Fock space. To quantify the degree of this Fock space localization, the multifractal dimension $D_q$ is employed; it has been claimed that $D_q$ shows a jump from the delocalized value $D_q=1$ in the ETH phase (ETH: eigenstate thermalization hypothesis) to a smaller value $0<D_q<1$ at the ETH-MBL transition, yet exhibiting a conspicuous discrepancy from the fully localized value $D_q=0$, which indicate that multifractality remains inside the MBL phase. Here, to better quantify the situation we employ, instead of the commonly used computational basis, the one-particle density matrix (OPDM) and use its eigenstates (natural orbitals) as a Fock state basis for representing many-body eigenstates $|psirangle$ of the system. Using this basis, we compute $D_q$ and other indices quantifying the Fock space localization, such as the local purity $S$, which is derived from the occupation spectrum ${n_alpha}$ (eigenvalues of the OPDM). We highlight the statistical distribution of Hamming distance $x_{munu}$ occurring in the pair-wise coefficients $|a_mu|^2|a_nu|^2$ in $S$, and compare this with a related quantity reported in the literature.","PeriodicalId":8438,"journal":{"name":"arXiv: Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81030413","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: 5

Thouless Energy Across Many-Body Localization Transition in Floquet Systems. Floquet系统多体局部化转换中的无能量。

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-31 DOI: 10.1103/PhysRevB.104.L081112

M. Sonner, Maksym Serbyn, Z. Papi'c, D. Abanin

引用次数: 8

Duality between two generalized Aubry-André models with exact mobility edges 具有精确迁移边的两个广义aubry - andr<s:1>模型之间的对偶性

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-17 DOI: 10.1103/PhysRevB.103.174205

Yucheng Wang, Xu Xia, Yongjian Wang, Zuohuan Zheng, Xiong-Jun Liu

引用次数: 13

Curvature-driven ac-assisted creep dynamics of magnetic domain walls 曲率驱动的交流辅助磁畴壁蠕变动力学

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-17 DOI: 10.1103/PhysRevB.103.L220409

P. Domenichini, F. Parı́s, M. Capeluto, M. Granada, J. George, G. Pasquini, A. Kolton

引用次数: 0

Relationship between two-level systems and quasi-localized normal modes in glasses 双能级系统与玻璃准定域法模的关系

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-14 DOI: 10.1103/PhysRevMaterials.5.055602

D. Khomenko, D. Reichman, F. Zamponi

引用次数: 8

Extended nonergodic regime and spin subdiffusion in disordered SU(2)-symmetric Floquet systems 无序SU(2)-对称Floquet系统的扩展非遍历区和自旋亚扩散

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-01 DOI: 10.1103/physrevb.102.214205

Zhi-Cheng Yang, Stuart Nicholls, M. Cheng

引用次数: 4

Transmission of waves through a pinned elastic medium 波通过固定弹性介质的传输

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-12-01 DOI: 10.1103/PhysRevB.103.224211

Tsuyoshi Yamamoto, L. Glazman, M. Houzet

引用次数: 1

Disorder in dissipation-induced topological states: Evidence for a different type of localization transition 耗散引起的拓扑状态的紊乱:一种不同类型的局域转换的证据

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-11-19 DOI: 10.1103/PhysRevB.103.L241401

Alon Beck, M. Goldstein

引用次数: 5

Suppression of transport anisotropy at the Anderson localization transition in three-dimensional anisotropic media 三维各向异性介质中安德森局域化转变中输运各向异性的抑制

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-11-18 DOI: 10.1103/physrevb.102.220201

Antton Goïcoechea, S. Skipetrov, J. Page

引用次数: 4

Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses 自旋电子突触的射频乘法和累加运算

arXiv: Disordered Systems and Neural Networks Pub Date : 2020-11-16 DOI: 10.1103/PHYSREVAPPLIED.15.034067

N. Leroux, Danijela Marković, Erwann Martin, Teodora Petrisor, D. Querlioz, A. Mizrahi, J. Grollier

{"title":"Radio-Frequency Multiply-and-Accumulate Operations with Spintronic Synapses","authors":"N. Leroux, Danijela Marković, Erwann Martin, Teodora Petrisor, D. Querlioz, A. Mizrahi, J. Grollier","doi":"10.1103/PHYSREVAPPLIED.15.034067","DOIUrl":"https://doi.org/10.1103/PHYSREVAPPLIED.15.034067","url":null,"abstract":"Exploiting the physics of nanoelectronic devices is a major lead for implementing compact, fast, and energy efficient artificial intelligence. In this work, we propose an original road in this direction, where assemblies of spintronic resonators used as artificial synapses can classify an-alogue radio-frequency signals directly without digitalization. The resonators convert the ra-dio-frequency input signals into direct voltages through the spin-diode effect. In the process, they multiply the input signals by a synaptic weight, which depends on their resonance fre-quency. We demonstrate through physical simulations with parameters extracted from exper-imental devices that frequency-multiplexed assemblies of resonators implement the corner-stone operation of artificial neural networks, the Multiply-And-Accumulate (MAC), directly on microwave inputs. The results show that even with a non-ideal realistic model, the outputs obtained with our architecture remain comparable to that of a traditional MAC operation. Us-ing a conventional machine learning framework augmented with equations describing the physics of spintronic resonators, we train a single layer neural network to classify radio-fre-quency signals encoding 8x8 pixel handwritten digits pictures. The spintronic neural network recognizes the digits with an accuracy of 99.96 %, equivalent to purely software neural net-works. This MAC implementation offers a promising solution for fast, low-power radio-fre-quency classification applications, and a new building block for spintronic deep neural net-works.","PeriodicalId":8438,"journal":{"name":"arXiv: Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80549577","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: 13