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Qiang Zhang

Publications and source records attributed to Qiang Zhang.

At least 505 records · Page 28Linked to original sources
arXiv2019

Steplike metamagnetic transitions in a honeycomb lattice antiferromagnet Tb$_2$Ir$_3$Ga$_9$

Single crystals of a honeycomb lattice antiferromagnet, Tb$_2$Ir$_3$Ga$_9$ were synthesized, and the physical properties have been studied. From magnetometry, a long-range antiferromagnetic ordering at $\approx$12.5 K with highly anisotropic magnetic behavior was found. Neutron powder diffraction confirms that the Tb spins lie along the $\va $-axis, parallel to the shortest Tb-Tb contact. Two field-induced spin-flip transitions are observed when the field is applied parallel to this axis, separated by a plateau corresponding roughly to M$\approx$M$_{\rm{s}}$/2. Transport measurements show the resistivity to be metallic with a discontinuity at the onset of Néel order. Heat capacity shows a $λ$-like transition confirming the bulk nature of the magnetism. We propose a phenomenological spin-Hamiltonian that describes the magnetization plateau as a result of strong Ising character arising from a quasidoublet ground state of the Tb$^{3+}$ ion in a site of \textit{C$_s$} symmetry and expressing a significant bond dependent anisotropy.

cond-mat.mes-hall↗
arXiv2019

Single-photon computational 3D imaging at 45 km

Long-range active imaging has a variety of applications in remote sensing and target recognition. Single-photon LiDAR (light detection and ranging) offers single-photon sensitivity and picosecond timing resolution, which is desirable for high-precision three-dimensional (3D) imaging over long distances. Despite important progress, further extending the imaging range presents enormous challenges because only weak echo photons return and are mixed with strong noise. Herein, we tackled these challenges by constructing a high-efficiency, low-noise confocal single-photon LiDAR system, and developing a long-range-tailored computational algorithm that provides high photon efficiency and super-resolution in the transverse domain. Using this technique, we experimentally demonstrated active single-photon 3D-imaging at a distance of up to 45 km in an urban environment, with a low return-signal level of $\sim$1 photon per pixel. Our system is feasible for imaging at a few hundreds of kilometers by refining the setup, and thus represents a significant milestone towards rapid, low-power, and high-resolution LiDAR over extra-long ranges.

eess.IV↗
arXiv2019

Gas-phase synthesis of carbon nanotube-graphene heterostructures

Graphene and carbon nanotubes (CNTs) share the same atomic structure of hexagonal carbon lattice. Yet, their synthesis differs in many aspects, including the shape and size of the catalyst. Here, we demonstrate a floating-catalyst chemical vapor deposition (FCCVD) technique for substrate-free, single-step growth of CNT-graphene heterostructures (CNT-G-H) using ethylene as a carbon source. The formation of CNT-G-H is directly evidenced by lattice-resolved (scanning) transmission electron microscopy (STEM) and electron diffraction experiments, corroborated by atomic force microscopy (AFM). Our experiments show the relative number density of graphene-nanoflakes can be tuned by optimizing the synthesis conditions. Since in the applied process the formation of the structures take place in gas-suspension, the as-synthesized CNT-G-H films can be deposited on any surface in ambient temperature with an arbitrary thickness. Moreover, this process of CNT-G-H synthesis with strong universality has also been realized in multiple systems of ethylene-based FCCVD with various catalysts and set-ups.

physics.app-ph↗
arXiv2019

Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-the-art performance on DVS128 Gesture Dataset.

cs.CV↗
arXiv2019

Learning Context Graph for Person Search

Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets.

cs.CV↗
arXiv2019

Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

cs.CV↗
arXiv2019

Local discontinuous Galerkin methods with explicit-implicit-null time discretizations for solving nonlinear diffusion problems

In this paper we discuss the local discontinuous Galerkin methods coupled with two specific explicit-implicit-null time discretizations for solving one-dimensional nonlinear diffusion problems $U_t=(a(U)U_x)_x$. The basic idea is to add and subtract two equal terms $a_0 U_{xx}$ on the right hand side of the partial differential equation, then to treat the term $a_0 U_{xx}$ implicitly and the other terms $(a(U)U_x)_x-a_0 U_{xx}$ explicitly. We give stability analysis for the method on a simplified model by the aid of energy analysis, which gives a guidance for the choice of $a_0$, i.e, $a_0 \ge \max\{a(u)\}/2$ to ensure the unconditional stability of the first order and second order schemes. The optimal error estimate is also derived for the simplified model, and numerical experiments are given to demonstrate the stability, accuracy and performance of the schemes for nonlinear diffusion equations.

math.NA↗
arXiv2019

Error-Disturbance Trade-off in Sequential Quantum Measurements

We derive a state dependent error-disturbance trade-off based on a statistical distance in the sequential measurements of a pair of noncommutative observables and experimentally verify the relation with a photonic qubit system. We anticipate that this Letter may further stimulate the study on the quantum uncertainty principle and related applications in quantum measurements.

quant-ph↗
arXiv2019

Experimental Quantum Switching for Exponentially Superior Quantum Communication Complexity

Finding exponential separation between quantum and classical information tasks is like striking gold in quantum information research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch---which creates a coherent superposition of the causal order of events, known as quantum causality---has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs $d$-dimensional quantum systems, qudits, up to $d=2^{13}$ dimensions and demonstrates a communication complexity advantage, requiring less than $(0.696 \pm 0.006)$ times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures.

quant-ph↗
arXiv2019

Enhanced Tunnelling in a Hybrid of Single-Walled Carbon Nanotubes and Graphene

Transparent and conductive films (TCFs) are of great technological importance. The high transmittance, electrical conductivity and mechanical strength make single-walled carbon nanotubes (SWCNTs) a good candidate for their raw material. Despite the ballistic transport in individual SWCNTs, however, the electrical conductivity of their networks is limited by low efficiency of charge tunneling between the tube elements. Here, we demonstrate that the nanotube network sheet resistance at high optical transmittance is decreased by more than 50% when fabricated on graphene and thus provides a comparable improvement as widely adopted gold chloride ($\mathrm{AuCl_3}$) doping. However, while Raman spectroscopy reveals substantial changes in spectral features of doped nanotubes, no similar effect is observed in presence of graphene. Instead, temperature dependent transport measurements indicate that graphene substrate reduces the tunneling barrier heights while its parallel conductivity contribution is almost negligible. Finally, we show that combining the graphene substrate and $\mathrm{AuCl_3}$ doping, the SWCNT thin films can exhibit sheet resistance as low as 36 $Ω$/sq. at 90% transmittance.

physics.app-ph↗
arXiv2019

Compact all-fiber polarization-independent up-conversion single-photon detector

We demonstrate a compact all-fiber polarization-independent up-conversion single-photon detector based on integrated reverse proton exchanged periodically poled lithium niobate waveguides. The horizontally and vertically polarized components of randomly polarized signals are separated with a fiber-coupled polarization beam splitter, launched into two orthogonally polarized polarization maintaining fibers and fetched into two adjacent independent waveguides on the same device. The up-converted outputs from both waveguide channels are then combined with a multi-mode fiber combiner and detected by a silicon detector. With this configuration, the polarization-independent single-photon counting at 1.55 um is achieved with a system detection efficiency of 29.3%, a dark count rate of 1600 counts per second, and a polarization dependent loss of 0.1dB. This compact all-fiber system is robust and has great application potential in practical quantum key distribution systems.

physics.ins-det↗
arXiv2019

Anomalous Magnetic Behavior in Ba2CoO4 with Isolated CoO4 Tetrahedra

The dimensionality of the electronic and magnetic structure of a given material is generally predetermined by its crystal structure. Here, using elastic and inelastic neutron scattering combined with magnetization measurements, we find unusual magnetic behavior in three-dimensional (3D) Ba2CoO4. In spite of isolated CoO4 tetrahedra, the system exhibits a 3D noncollinear antiferromagnetic order in the ground state with an anomalously large Curie-Weiss temperature of 110 K compared to TN = 26 K. More unexpectedly, spin dynamics displays quasi-2D spin wave dispersion with an unusually large spin gap, and 1D magnetoelastic coupling. Our results indicate that Ba2CoO4 is a unique system for exploring the interplay between isolated polyhedra, low-dimensional magnetism, and novel spin states in oxides.

cond-mat.str-el↗
arXiv2019

Up-conversion single-photon detectors based on integrated periodically poled lithium niobate waveguides

We demonstrate up-conversion single-photon detectors based on integrated periodically poled lithium niobate waveguides, which incorporate two mode filters and a directional coupler. The two mode filters are optimized for the fiber-waveguide coupling efficiencies for 1550 nm and 1950 nm respectively while the directional coupler plays the role of wavelength combiner, making the overall system portable and low-cost. The two wavelengths pump each other in our detection system. We achieve detection efficiencies of 28% for 1550 nm and 27% for 1950 nm, respectively. This scheme provides an efficient integrated single-photon detection method for any two well-separated spectral bands in the whole low-loss range of lithium niobate waveguides.

physics.ins-det↗
arXiv2019

Quasi-2D magnetism and origin of the Dirac semimetallic behavior in nonstoichiometric Sr$_{1-y}$Mn$_{1-z}$Sb$_{2}$ (y, z~$<$0.1)

Nonstoichiometric Sr$_{1-y}$Mn$_{1-z}$Sb$_{2}$ (y, z~$<$0.1) is known to exhibit a coexistence of magnetic order and the nontrivial semimetallic behavior related to Dirac or Weyl fermions. Here, we report inelastic neutron scattering analyses of the spin dynamics and density functional theory studies on the electronic properties of Sr$_{1-y}$Mn$_{1-z}$Sb$_{2}$. We observe a relatively large spin excitation gap $\approx$ 8.5 meV at 5 K, and the interlayer magnetic exchange constant only 2.8 \% of the dominant intralayer magnetic interaction, providing evidence that Sr$_{1-y}$Mn$_{1-z}$Sb$_{2}$ exhibits a quasi-2D magnetism. Using density functional theory, we find a strong influence of magnetic orders on the electronic band structure and the Dirac dispersions near the Fermi level along the Y-S direction in the presence of a ferromagnetic ordering. Our study unveils novel interplay between the magnetic order, magnetic transition, and electronic property in Sr$_{1-y}$Mn$_{1-z}$Sb$_{2}$, and opens new pathways to control the relativistic band structure through magnetism in ternary compounds.

cond-mat.str-el↗
arXiv2019

Structure-Property Relationship in Layered BaMn2Sb2 and Ba2Mn3Sb2O2

Layered transition-metal compounds have received great attention owing to their novel physical properties. Here, we present the structural, electronic, thermal, and magnetic properties of BaMn2Sb2 and Ba2Mn3Sb2O2 single crystals, both with the layered structure analogous to high-temperature superconductors. While the Mn moment in the MnSb4 tetrahedral environment forms G-type antiferromagnetic (AFM) ordering in both BaMn2Sb2 (TN1~443 K) and Ba2Mn3Sb2O2 (TN1~314 K), a short-range AFM order is found in the intercalated MnO2 layer at a much lower temperature (TN2~60 K) in Ba2Mn3Sb2O2. The directions of the ordered moments in these two magnetic sub-lattices of Ba2Mn3Sb2O2 are perpendicular to each other, even though the system is electrically conductive. This indicates that the large magnetic moments in these compounds are highly localized, leading to negligible coupling between MnSb4 and MnO2 layers in Ba2Mn3Sb2O2. These findings provide an insight into the structure-magnetism-based design principle for new superconductors.

cond-mat.str-el↗
arXiv2018

Experimental demonstration of nonbilocality with truly independent sources and strict locality constraints

Entanglement swapping entangles two particles that have never interacted[1], which implicitly assumes that each particle carries an independent local hidden variable, i.e., the presence of bilocality[2]. Previous experimental studies of bilocal hidden variable models did not fulfill the central requirement that the assumed two local hidden variable models must be mutually independent and hence their conclusions are flawed on the rejection of local realism[3-5]. By harnessing the laser phase randomization[6] rising from the spontaneous emission to the stimulated emission to ensure the independence between entangled photon-pairs created at separate sources and separating relevant events spacelike to satisfy the no-signaling condition, for the first time, we simultaneously close the loopholes of independent source, locality and measurement independence in an entanglement swapping experiment in a network. We measure a bilocal parameter of 1.181$\pm$0.004 and the CHSH game value of 2.652$\pm$0.059, indicating the rejection of bilocal hidden variable models by 45 standard deviations and local hidden variable models by 11 standard deviations. We hence rule out local realism and justify the presence of quantum nonlocality in our network experiment. Our experimental realization constitutes a fundamental block for a large quantum network. Furthermore, we anticipate that it may stimulate novel information processing applications[7,8].

quant-ph↗
arXiv2018

Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

We propose a new sampler that integrates the protocol of parallel tempering with the Nosé-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially facilitates deep Bayesian learning on large datasets where complex multimodal posteriors and mini-batch gradient are encountered.

stat.ML↗