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The schemes proposed by S. Bose et al [Phys. Rev. Lett. 83, 5158 (1999)] and others for long distance atomic teleportation using cavity decay, gives message state dependent fidelity on successful attempt and in case of failure message state…

Quantum Physics · Physics 2012-09-17 Manoj K. Mishra , Hari Prakash

We systematically study the parity- and time-reversal (PT) symmetric non-Hermitian version of a quantum network proposed in the paper of Christandl et al. [Phys. Rev. Lett. 92, 187902 (2004)]. The nature of this model shows that it is a…

Quantum Physics · Physics 2013-04-16 X. Z. Zhang , L. Jin , Z. Song

The conductance of disordered wires with symplectic symmetry is studied by a random-matrix approach. It has been believed that Anderson localization inevitably arises in ordinary disordered wires. A counterexample is recently found in the…

Mesoscale and Nanoscale Physics · Physics 2009-11-11 Hiroshi Sakai , Yositake Takane

The development of four-dimensional (4D) scanning transmission electron microscopy (STEM) using fast detectors has opened-up new avenues for addressing some of long-standing challenges in electron imaging. One of these challenges is how to…

Materials Science · Physics 2021-01-19 Yu-Tsun Shao , Renliang Yuan , Haw-Wen Hsiao , Qun Yang , Yang Hu , Jian-Min Zuo

Let $G$ be a graph with adjacency matrix $A$. The transition matrix of $G$ relative to $A$ is defined by $H_{A}(t):=\exp{(-itA)},\;t\in\Rl$. We say that the graph $G$ admits perfect state transfer between the verteices $u$ and $v$ at…

Combinatorics · Mathematics 2019-01-08 Hiranmoy Pal , Bikash Bhattacharjya

The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC). In this…

Signal Processing · Electrical Eng. & Systems 2020-10-29 Xiaoting Wang , Xiaozhe Wang , Hao Sheng , Xi Lin

The scattering properties of randomly layered optical media with ${\cal PT}$-symmetric index of refraction are studied using the transfer-matrix method. We find that the transmitance decays exponentially as a function of the system size,…

Optics · Physics 2012-05-10 Samuel Kalish , Zin Lin , Tsampikos Kottos

Solid-state spin defects are attractive candidates for developing quantum sensors and simulators. The spin and charge degrees of freedom in large defect ensembles are a promising platform to explore complex many-body dynamics and the…

Recently M. Horodecki, P. Horodecki and R. Horodecki have introduced a set of density matrices of two spin-1 particles from which it is not possible to distill any maximally entangled states, even though the density matrices are entangled.…

Quantum Physics · Physics 2009-10-31 N Linden , S Popescu

In numerical simulations of classical and quantum lattice systems, 2d corner transfer matrices (CTMs) and 3d corner tensors (CTs) are a useful tool to compute approximate contractions of infinite-size tensor networks. In this paper we show…

Strongly Correlated Electrons · Physics 2017-09-26 Ching-Yu Huang , Tzu-Chieh Wei , Roman Orus

Charge transport in disordered two-dimensional (2D) systems showcases a myriad of unique phenomenologies that highlight different aspects of the underlying quantum dynamics. Electrons in such systems undergo a crossover from ballistic…

Mesoscale and Nanoscale Physics · Physics 2025-11-18 H. P. Veiga , S. M. João , J. M. Alendouro Pinho , J. P. Santos Pires , J. M. Viana Parente Lopes

We investigate the phase transition of the dodecahedron model on the square lattice. The model is a discrete analogue of the classical Heisenberg model, which has continuous $O(3)$ symmetry. In order to treat the large on-site degree of…

Statistical Mechanics · Physics 2020-09-23 Hiroshi Ueda , Kouichi Okunishi , Seiji Yunoki , Tomotoshi Nishino

Decentralized network theories focus on achieving consensus and in speeding up the rate of convergence to consensus. However, network cohesion (i.e., maintaining consensus) during transitions between consensus values is also important when…

Robotics · Computer Science 2021-02-19 Yoshua Gombo , Anuj Tiwari , Santosh Devasia

Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly…

Machine Learning · Computer Science 2026-02-18 Tom Trigano , Yann Sepulcre , Itshak Lapidot

We propose a hypercube switching architecture for the perfect state transfer (PST) where we prove that it is always possible to find an induced hypercube in any given hypercube of any dimension such that PST can be performed between any two…

Quantum Physics · Physics 2020-07-07 Siddhant Singh

A class of one-dimensional lattice models with incommensurate complex potential $V(\theta)=2[\lambda_r cos(\theta)+i \lambda_i sin(\theta)]$ is found to exhibit localization transition at $|\lambda_r|+|\lambda_i|=1$. This transition from…

Disordered Systems and Neural Networks · Physics 2009-10-31 Amin Jazaeri , Indubala I. Satija

We show that finite-size, disordered molecular networks can mediate highly efficient, coherent excitation transfer which is robust against ambient dephasing and associated with strong multi-site entanglement. Such optimal, random molecular…

Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions (SDEs). Despite strong empirical performance, standard flow-matching objectives…

Machine Learning · Computer Science 2026-02-27 Chika Maduabuchi

We review recent progress in understanding the different spatial broken symmetries that occur in the normal states of the family of charge-transfer solids (CTS) that exhibit superconductivity (SC), and discuss how this knowledge gives…

Superconductivity · Physics 2019-02-11 R. T. Clay , S. Mazumdar

The feature map obtained from the denoising autoencoder (DAE) is investigated by determining transportation dynamics of the DAE, which is a cornerstone for deep learning. Despite the rapid development in its application, deep neural…

Machine Learning · Computer Science 2017-12-13 Sho Sonoda , Noboru Murata