arXiv subjects

Qiang Zhou

Publications and source records attributed to Qiang Zhou.

At least 145 records · Page 8Linked to original sources

arXiv2019

Measurement-device-independent quantum key distribution coexisting with classical communication

The possibility for quantum and classical communication to coexist on the same fibre is important for deployment and widespread adoption of quantum key distribution (QKD) and, more generally, a future quantum internet. While coexistence has been demonstrated for different QKD implementations, a comprehensive investigation for measurement-device independent (MDI) QKD -- a recently proposed QKD protocol that cannot be broken by quantum hacking that targets vulnerabilities of single-photon detectors -- is still missing. Here we experimentally demonstrate that MDI-QKD can operate simultaneously with at least five 10 Gbps bidirectional classical communication channels operating at around 1550 nm wavelength and over 40 km of spooled fibre, and we project communication rates in excess of 10 THz when moving the quantum channel from the third to the second telecommunication window. The similarity of MDI-QKD with quantum repeaters suggests that classical and generalised quantum networks can co-exist on the same fibre infrastructure.

quant-ph↗

arXiv2019

Theoretical investigation of a spectrally pure-state generation from isomorphs of KDP crystal at near-infrared and telecom wavelengths

Spectrally uncorrelated biphoton state generated from the spontaneous nonlinear optical process is an important resource for quantum information. Currently such spectrally uncorrelated biphoton state can only be prepared from limited kinds of nonlinear media, thus limiting their wavelengths. In order to explore wider wavelength range, here we theoretically study the generation of spectrally uncorrelated biphoton state from 14 isomorphs of potassium dihydrogen phosphate (KDP) crystal. We find that 11 crystals from the `KDP family' still maintain similar nonlinear optical properties of KDP, such as KDP, DKDP, ADP, DADP, ADA, DADA, RDA, DRDA, RDP, DRDP and KDA, which satisfy 3 kinds of the group-velocity matching conditions for spectrally uncorrelated biphoton state generation from near-infrared to telecom wavelengths. Based on the uncorrelated biphoton state, we investigate the generation of heralded pure-state single photon by detecting one member of the biphoton state to herald the output of the other. The purity of the heralded single photon is as high as 0.98 without using a narrow-band filter; the Hong-Ou-Mandel interference from independent sources can also achieve a visibility of 98%. This study may provide more and better single-photon sources for quantum information processing at near-infrared and telecom wavelengths.

quant-ph↗

arXiv2018

Theoretical analyses of the sub-grid quantities' effect on filtered Eulerian drag force

An expression of the filtered Eulerian drag force is proposed based on the second order Taylor polynomial approximation of the microscopic Eulerian drag coefficient. Theoretical computations of the expression are performed at low Reynolds numbers based on an available microscopic drag model. It shows that four sub-grid quantities, i.e., the gas drift velocity, the solid drift velocity, the scalar variance of solid volume fraction and a third-order moment, defined as the covariance of squared solid volume fraction and the slip velocity, are significant for an accurate estimation of the filtered drag force at low Reynolds numbers. The gas drift velocity is nothing but the drift velocity defined by Parmentier et al. (AIChE Journal, 2012, 58 (4): 1084-1098), and in analogy to the gas drift velocity, we defines the solid drift velocity in the present work. The great relevance of the gas drift velocity and the scalar variance of solid volume fraction to the filtered drag force have been demonstrated through numerous correlative analyses of fully resolved simulations. Present theoretical analysis shows that the gas drift velocity term is primarily responsible for the drag reduction, whereas the solid drift velocity term is prone to attenuate this reduction, especially at moderate and high solid volume fractions. The scalar variance of solid volume fraction term is found to increase the filtered drag force in the full range of solid volume fractions. The third-order term has exactly the same coefficient with that of the variance term, and it mostly attains negative values with the trend to decrease the filtered drag force.

physics.flu-dyn↗

arXiv2018

Practical quantum random number generator based on sampling vacuum fluctuations

Random number generation is an enabling technology for fields as varied as Monte Carlo simulations and quantum information science. An important application is a secure quantum key distribution (QKD) system; here, we propose and demonstrate an approach to random number generation that satisfies the specific requirements for QKD. In our scheme, vacuum fluctuations of the electromagnetic-field inside a laser cavity are sampled in a discrete manner in time and amplified by injecting current pulses into the laser. Random numbers can be obtained by interfering the laser pulses with another independent laser operating at the same frequency. Using only off-the-shelf opto-electronics and fibre-optics components at 1.5 $μ$m wavelength, we experimentally demonstrate the generation of high-quality random bits at a rate of up to 1.5 GHz. Our results show the potential of the new scheme for practical information processing applications.

quant-ph↗

arXiv2018

Transport equation for the drift velocity in predicting the filtered Eulerian drag force: a theoretical development

The drift velocity has been proven to have significant relevance to the filtered Eulerian drag force by numerous correlative analyses of fully resolved simulations. It is a sub-grid quantity defined as the difference between the filtered gas velocity seen by the particle phase and the resolved filtered gas velocity. In literature, it is shown that various algebraic models for the drift velocity fail to give entirely satisfactory prediction of the filtered drag force. Unlike previous works, we theoretically derived the transport equation for the drift velocity from the standard two-fluid model (TFM) equations without any additional assumptions. The new approach, though requires additional closures for the new unresolved terms, is believed to have the potential to improve the precision of the estimation of this sub-grid quantity and hence give better prediction of the filtered drag force for gas-solid fluidization problems.

physics.flu-dyn↗

arXiv2018

Composite Inference for Gaussian Processes

Large-scale Gaussian process models are becoming increasingly important and widely used in many areas, such as, computer experiments, stochastic optimization via simulation, and machine learning using Gaussian processes. The standard methods, such as maximum likelihood estimation (MLE) for parameter estimation and the best linear unbiased predictor (BLUP) for prediction, are generally the primary choices in many applications. In spite of their merits, those methods are not feasible due to intractable computation when the sample size is huge. A novel method for the purposes of parameter estimation and prediction is proposed to solve the computational problems of large-scale Gaussian process based models, by separating the original dataset into tractable subsets. This method consistently combines parameter estimation and prediction by making full use of the dependence among conditional densities: a statistically efficient composite likelihood based on joint distributions of some well selected conditional densities is developed to estimate parameters and then "composite inference" is coined to make prediction for an unknown input point, based on its distributions conditional on each block subset. The proposed method transforms the intractable BLUP into a tractable convex optimization problem. It is also shown that the prediction given by the proposed method, called the best linear unbiased block predictor, has a minimum variance for a given separation of the dataset. Keywords: Large scale, Parallel computing, Composite likelihood, Spatial process

stat.ME↗

arXiv2018

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary- aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model will be publicly available at https://wywu.github.io/projects/LAB/LAB.html

cs.CV↗

arXiv2018

Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation

Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips. Efficient obfuscation implementations are expected with lower design complexity and overhead but higher attack difficulties. In this paper, an emerging obfuscation approach is proposed by leveraging spinorbit torque (SOT) devices based look-up-tables (LUTs) as reconfigurable logic to replace the carefully selected gates. It is essentially impossible to identify the obfuscated gate with SOTs inside according to the physical geometry characteristics because the configured functionalities are represented by magnetization states. Such an obfuscation approach makes the circuit security further improved with high exponential attack complexities. Experiments on MCNC and ISCAS 85/89 benchmark suits show that the proposed approach could reduce the area overheads due to obfuscation by 10% averagely.

cs.ET↗

arXiv2017

A cost-effective measurement-device-independent quantum key distribution system for quantum networks

We experimentally realize a measurement-device-independent quantum key distribution (MDI-QKD) system based on cost-effective and commercially available hardware such as distributed feedback (DFB) lasers and field-programmable gate arrays (FPGA) that enable time-bin qubit preparation and time-tagging, and active feedback systems that allow for compensation of time-varying properties of photons after transmission through deployed fibre. We examine the performance of our system, and conclude that its design does not compromise performance. Our demonstration paves the way for MDI-QKD-based quantum networks in star-type topology that extend over more than 100 km distance.

quant-ph↗

arXiv2016

Direct Meissner Effect Observation of Superconductivity in Compressed H2S

Recently, an extremely high superconducting temperature (Tc) of ~200 K has been reported in the sulfur hydride system above 100 GPa. This result is supported by theoretical predictions and verified experimentally. The crystal structure of the superconducting phase was also identified experimentally, confirming the theoretically predicted structure as well as a decomposition mechanism from H2S to H3S+S. Even though nuclear resonant scattering has been successfully used to provide magnetic evidence for a superconducting state, a direct measurement of the important Meissner effect is still lacking. Here we report in situ alternating-current magnetic susceptibility measurements on compressed H2S under high pressures. It is shown that superconductivity suddenly appears at 117 GPa and that Tc reaches 183 K at 149 GPa before decreasing monotonically with a further increase in pressure. This evolution agrees with both theoretical calculations and earlier experimental measurements. The idea of conventional high temperature superconductivity in hydrogen-dominant compounds has thus been realized in the sulfur hydride system under hydrostatic pressure, opening further exciting perspectives for possibly realizing room temperature superconductivity in hydrogen-based compounds.

cond-mat.supr-con↗

arXiv2016

Physical parameter study of eight W Ursae Majoris-type contact binaries in NGC 188

We used the newly commissioned 50 cm Binocular Network (50BiN) telescope at Qinghai Station of Purple Mountain Observatory (Chinese Academy of Sciences) to observe the old open cluster NGC 188 in V and R as part of a search for variable objects. Our time-series data span a total of 36 days. Radial velocity and proper-motion selection resulted in a sample of 532 genuine cluster members. Isochrone fitting was applied to the cleaned cluster sequence, yielding a distance modulus of (m - M)0V = 11.35 \pm 0.10 mag and a total foreground reddening of E(V - R) = 0.062 \pm 0.002 mag. Light-curve solutions were obtained for eight W Ursae Majoris eclipsing-binary systems (W UMas) and their orbital parameters were estimated. Using the latter parameters, we estimate a distance to the W UMas which is independent of the host cluster's physical properties. Based on combined fits to six of the W UMas (EP Cep, EQ Cep, ES Cep, V369 Cep, and--for the first time--V370 Cep and V782 Cep), we obtain an average distance modulus of (m - M)0V = 11.31 \pm 0.08 mag, which is comparable with that resulting from our isochrone fits. These six W UMas exhibit an obvious period-luminosity relation. We derive more accurate physical parameters for the W UMa systems and discuss their initial masses and ages. The former show that these W UMa systems have likely undergone angular-momentum evolution within a convective envelope (W-type evolution). The ages of the W UMa systems agree well with the cluster's age.

astro-ph.SR↗

arXiv2016

Quantum teleportation across a metropolitan fibre network

If a photon interacts with a member of an entangled photon pair via a so-called Bell-state measurement (BSM), its state is teleported over principally arbitrary distances onto the second member of the pair. Starting in 1997, this puzzling prediction of quantum mechanics has been demonstrated many times; however, with one very recent exception, only the photon that received the teleported state, if any, travelled far while the photons partaking in the BSM were always measured closely to where they were created. Here, using the Calgary fibre network, we report quantum teleportation from a telecommunication-wavelength photon, interacting with another telecommunication photon after both have travelled over several kilometres in bee-line, onto a photon at 795~nm wavelength. This improves the distance over which teleportation takes place from 818~m to 6.2~km. Our demonstration establishes an important requirement for quantum repeater-based communications and constitutes a milestone on the path to a global quantum Internet.

quant-ph↗

arXiv2016

Crossover from Metal to Insulator in Dense Lithium-Rich Compound $\textrm{CLi}_{4}$

Crystal structures of $\textrm{CLi}_4$ compounds are explored through \emph{ab} \emph{initio} evolutionary methodology. Phase transition from metal to semimetal and semiconductor, and eventually to insulator with increasing pressure are revealed under pressure. Pressure-induced evolution of anti-metallization has been described quantitatively by Fermi Surface Filling Ratio and electron energy band gap using \emph{ab} \emph{initio} \emph{GW} calculations. Anti-metallization is attributed to the hybrid valence electrons and their repulsion by core electrons into the lattice interstices. Very weak electron-phonon coupling interactions are found in the metallic phases, resulting in very low superconducting temperature.

cond-mat.mtrl-sci↗

arXiv2015

A multiplexed light-matter interface for fibre-based quantum networks

Processing and distributing quantum information using photons through fibre-optic or free-space links is essential for building future quantum networks. The scalability needed for such networks can be achieved by employing photonic quantum states that are multiplexed into time and/or frequency, and light-matter interfaces that are able to store and process such states with large time-bandwidth product and multimode capacities. Despite important progress in developing such devices, the demonstration of these capabilities using non-classical light remains challenging. Employing the atomic frequency comb quantum memory protocol in a cryogenically cooled erbium-doped optical fibre, we report the quantum storage of heralded single photons at a telecom-wavelength (1.53 μm) with a time-bandwidth product approaching 800. Furthermore we demonstrate frequency-multimode storage as well as memory-based spectral-temporal photon manipulation. Notably, our demonstrations rely on fully integrated quantum technologies operating at telecommunication wavelengths, i.e. a fibre-pigtailed nonlinear waveguide for the generation of heralded single photons, an erbium-doped fibre for photon storage and manipulation, and fibre interfaced superconducting nanowire devices for efficient single photon detection. With improved storage efficiency, our light-matter interface may become a useful tool in future quantum networks.

quant-ph↗

arXiv2015

Parallel Knowledge Embedding with MapReduce on a Multi-core Processor

This article firstly attempts to explore parallel algorithms of learning distributed representations for both entities and relations in large-scale knowledge repositories with {\it MapReduce} programming model on a multi-core processor. We accelerate the training progress of a canonical knowledge embedding method, i.e. {\it translating embedding} ({\bf TransE}) model, by dividing a whole knowledge repository into several balanced subsets, and feeding each subset into an individual core where local embeddings can concurrently run updating during the {\it Map} phase. However, it usually suffers from inconsistent low-dimensional vector representations of the same key, which are collected from different {\it Map} workers, and further leads to conflicts when conducting {\it Reduce} to merge the various vectors associated with the same key. Therefore, we try several strategies to acquire the merged embeddings which may not only retain the performance of {\it entity inference}, {\it relation prediction}, and even {\it triplet classification} evaluated by the single-thread {\bf TransE} on several well-known knowledge bases such as Freebase and NELL, but also scale up the learning speed along with the number of cores within a processor. So far, the empirical studies show that we could achieve comparable results as the single-thread {\bf TransE} performs by the {\it stochastic gradient descend} (SGD) algorithm, as well as increase the training speed multiple times via adapting the {\it batch gradient descend} (BGD) algorithm for {\it MapReduce} paradigm.

cs.DC↗

arXiv2015

Distant Supervision for Entity Linking

Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages). In this way, a large scale of weakly labeled data can be generated without manual annotation and fed to a classifier for linking more newly discovered entities. Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia. Specifically, the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities. Experiments conducted on a dataset of 140,000 items and 60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze the feature performance and improve the F1-measure to 0.545.

cs.CL↗

arXiv2015

Probabilistic Belief Embedding for Knowledge Base Completion

This paper contributes a novel embedding model which measures the probability of each belief $\langle h,r,t,m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$). It facilitates knowledge completion by means of simple vector operations to discover new beliefs. Given an imperfect belief, we can not only infer the missing entities, predict the unknown relations, but also tell the plausibility of the belief, just leveraging the learnt embeddings of remaining evidences. To demonstrate the scalability and the effectiveness of our model, we conduct experiments on several large-scale repositories which contain millions of beliefs from WordNet, Freebase and NELL, and compare it with other cutting-edge approaches via competing the performances assessed by the tasks of entity inference, relation prediction and triplet classification with respective metrics. Extensive experimental results show that the proposed model outperforms the state-of-the-arts with significant improvements.

cs.AI↗

arXiv2015

Large Margin Nearest Neighbor Embedding for Knowledge Representation

Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.

cs.AI↗