Application Of Computation Research Articles

Research progress of intrinsic magnetic topological insulator MnBi<sub>2</sub>Te<sub>4</sub>

The interaction between non-trivial topological states and the magnetic order of intrinsic magnetic topological insulators gives rise to various exotic physical properties, including the quantum anomalous Hall effect and axion insulator. These materials possess great potential applications in low-power topological spintronic devices and topological quantum computation. Since the first intrinsic magnetic topological insulator, MnBi<sub>2</sub>Te<sub>4</sub>, was discovered in 2019, this material system has received significant attention from researchers and sparked a research boom. This paper begins with discussing the fundamental properties of MnBi<sub>2</sub>Te<sub>4</sub> and then turns to important research findings related to this intrinsic magnetic topological insulator. Specifically, it focuses on the quantum anomalous Hall effect, axion insulating state, and Majorana zero energy mode exhibited by the MnBi<sub>2</sub>Te<sub>4</sub> series. Furthermore, this paper highlights other research directions and current challenges associated with this material system. Finally, this paper provides a summary and outlook for future research on MnBi<sub>2</sub>Te<sub>4</sub>, aiming to offer valuable references for researchers in related fields.

Finite element mesh refinement for in-plane and out-of-plane vibration of variable geometrical Timoshenko beams based on superconvergent vibration modes

PurposeIn this paper, a superconvergent patch recovery method is proposed for superconvergent solutions of modes in the finite element post-processing stage of variable geometrical Timoshenko beams. The proposed superconvergent patch recovery method improves the solution speed and accuracy of the finite element analysis of a curved beam. The free vibration and natural frequency of the beam were considered for studying forced vibrations and structural resonance. Beam vibration mode analysis was performed for high-precision vibration mode solutions and frequency values. The proposed method can be used to compute beam vibration modes of beams with different shapes and boundary conditions as well as variable cross sections and curvatures. The purpose of this paper is to address these issues.Design/methodology/approachAn adaptive method was proposed to analyse the in-plane and out-of-plane free vibrations of the variable geometrical Timoshenko beams. In the post-processing stage of the displacement-based finite element method, the superconvergent patch recovery method and high-order shape function interpolation technique were used to obtain the superconvergent solution of mode (displacement). The superconvergent solution of mode was used to estimate the error of the finite element solution of mode in the energy form under the current mesh. Furthermore, an adaptive mesh refinement was proposed by mesh subdivision to derive an optimised mesh and accurate finite element solution to meet the preset error tolerance.FindingsThe results computed using the proposed algorithm were in good agreement with those computed using other high-precision algorithms, thus validating the accuracy of the proposed algorithm for beam analysis. The numerical analysis of parabolic curved beams, beams with variable cross sections and curvatures, elliptically curved beams and circularly curved beams helped verify that the solutions of frequencies were consistent with the results obtained using other specially developed methods. The proposed method is well suited for the mesh refinement analysis of a curved beam structure for analysing the changes in high-order vibration mode. The parts where the vibration mode changed significantly were locally densified; a relatively fine mesh division was adopted that validated the reliability of the mesh optimisation processing of the proposed algorithm.Originality/valueThe proposed algorithm can obtain high-precision vibration solutions of variable geometrical Timoshenko beams based on more optimized and reasonable meshes than the conventional finite element method. Furthermore, it can be used for vibration problems of parabolic curved beams, beams with variable cross sections and curvatures, elliptically curved beams and circularly curved beams. The proposed algorithm can be extended for application in superconvergent computation and adaptive analysis of finite element solutions of general structures and solid deformation fields and used for adaptive analysis of more complex plates, shells and three-dimensional structures. Additionally, this method can analyse the vibration and stability of curved members with crack damage to obtain high-precision vibration modes and instability modes under damage defects.

Generating Many Majorana Corner Modes and Multiple Phase Transitions in Floquet Second-Order Topological Superconductors

A d-dimensional, nth-order topological insulator or superconductor has localized eigenmodes at its (d−n)-dimensional boundaries (n≤d). In this work, we apply periodic driving fields to two-dimensional superconductors, and obtain a wide variety of Floquet second-order topological superconducting (SOTSC) phases with many Majorana corner modes at both zero and π quasienergies. Two distinct Floquet SOTSC phases are found to be separated by three possible kinds of transformations, i.e., a topological phase transition due to the closing/reopening of a bulk spectral gap, a topological phase transition due to the closing/reopening of an edge spectral gap, or an entirely different phase in which the bulk spectrum is gapless. Thanks to the strong interplay between driving and intrinsic energy scales of the system, all the found phases and transitions are highly controllable via tuning a single hopping parameter of the system. Our discovery not only enriches the possible forms of Floquet SOTSC phases, but also offers an efficient scheme to generate many coexisting Majorana zero and π corner modes, which may find applications in Floquet quantum computation.

Tunable vortex Majorana modes controlled by strain in homogeneous LiFeAs

The iron-based superconductors (FeSCs) have recently emerged as a promising single-material Majorana platform by hosting isolated Majorana zero modes (MZMs) at relatively high temperatures. To further verify its Majorana nature and move forward to build topological quantum qubits, it is highly desirable to achieve tunability for MZMs on homogeneous FeSCs. Here, with an in-situ strain device, we can controllably create MZMs on the homogeneous surface of stoichiometric superconductor LiFeAs by altering its chemical potential. The evolution of discrete energy modes inside a strained vortex is found to mimic exactly as the predicted topological vortex case, proving the Majorana nature of emerging zero modes of vortex. More importantly, our work provides a controllable method for MZM in a homogeneous FeSC, and such achievement of tunability of MZMs in the FeSC Majorana-material platform is an important step towards their application in topological quantum computation.

Simplexity testbed: A model-based digital twin testbed

In the last few years, due to the advances of computation, network, and wide-spread applications of artificial intelligence and machine learning techniques, developing and benefiting from the digital twin technology to connect the virtual to the physical becomes promising and practically feasible; therefore, a lot of attention has been attracted across industries of various domains. However, there lacks of platforms in the academic to facilitate research activities in digital twin technologies. This shortage has non-eligible and negative impact not only on advancing digital twin technologies, but also leading to scarcely publicly-available datasets for conducting research. To this end, in this paper, we present Simplexity Testbed. Simplexity Testbed is equipped with a physical model — an indoor ”driving ground” featuring various driving scenarios and surface conditions, and four land rovers (named SiLaRs) of two different types. Most importantly, the digital twin model of Simplexity Testbed currently is an integration of multiple models developed with different modeling paradigms (i.e., SysML enhanced with uncertainty information, Modelica, 3D simulators for autonomous driving), which enables model executions, simulations, and cross-model interactions. The architecture of Simplexity Testbed enables future integration of other modeling paradigms. In the paper, we also share our process of developing Simplexity Testbed and lessons learnt. In addition, we put lights on how various research activities can be enabled with Simplexity Testbed. We consider that with Simplexity Testbed, context-aware, autonomous, and adaptive capabilities of digital twins can be studied, which lead to full-fledged applications of digital twins in industry.

Evolutionary Computing to solve product inconsistencies in Software Product Lines

• Inconsistencies prevent and delay the configuration of an SPL product. • Evolutionary computation is proposed to resolve inconsistencies in SPL products. • ga-SPLIS (genetic algorithm-Software Product LIne Solution) is developed. • Results show the applicability of GA to resolve the SPL product inconsistencies. In Software Product Lines (SPLs), multiple design teams work collectively to configure products. Often, having multiple sub-designs leads to inconsistencies which prevent and delay the configuration of the final product at the end. Solutions are available to identify and resolve these inconsistencies for limited scale SPLs. For large scale and industrial SPLs, resolution of inconsistencies is still a potential research topic. In this paper, we propose, implement and validate an application of evolutionary computation, particularly genetic algorithms, to resolve inconsistencies in large scale SPL products. We label our approach ga -SPLIS (genetic algorithm-Software Product LIne Solution). Using a concrete experimental methodology with different feature set sizes and multiple standard performance metrics, we generate results which validate the applicability of ga -SPLIS for the SPL community.

Automation industry in cyber physical systems based on 4G IoT networks for operation controlling and monitoring management

Cyber-Physical System (CPS) is an emerging technique that focuses on the integration of computation applications as a network of communicating physical and cyber components which controls real time physical substructures of the industrial automation. Since implementation, operation and design of CPS and management of automation substructures are plays major significance in the various industrial applications. This study presents a Deep learning (DL) based intrusion controlling and monitoring management system (DL-ICMMS) for CPS in the automation industry for the detection of the occurrence of intrusions. To transform the input data in a compatible manner, data normalization is carried out and the Adam optimizer is applied to handle hyperparameter values of Restricted Boltzmann Machine (RBM) method. Experimental results analysis stated that the DL-ICMMS method increased accuy of 98.80% whereas the Multilayer Perceptron (MLP), K Nearest Neighbor (KNN), Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Long short-term memory (LSTM) approaches have shown reduced accuy of 93.10%, 93.62%, 91.60%, 90.88%, and 91.75%, correspondingly.

Reconfigurable dynamics of optical soliton molecular complexes in an ultrafast thulium fiber laser

Dissipative optical solitons of particle-like structures can form soliton molecules with striking molecule-like interactions. However, the experimental investigation of soliton molecules in ultrafast lasers is still largely confined to bound states of only two solitons and is far from being explored for soliton molecular complexes. Here, the reconfigurable switching of soliton molecular complexes originating from the binding of two soliton-pair molecules was demonstrated around 2 μm, simultaneously opening an emerging window in the longer wavelength. The periodic switching of soliton molecules with dual-stability can be resolved. Moreover, the single and multiple successively switching of soliton molecules can be triggered by the collision of drifting soliton or control over saturable absorption parameters. Further, the consecutive reversible switching with high fidelity is verified in soliton molecules with periodic pump modulation. These results unveil the dynamical soliton interaction and motivate potential applications in ultrafast spectroscopy, information encoding, and computations.

Optimized architectures for universal quantum state transformations using photonic path and polarization

An arbitrary lossless transformation in high-dimensional (HD) quantum space can be decomposed into elementary operations which are easy to implement, and an effective decomposition algorithm is important for constructing HD systems. Here, we present two optimized architectures to effectively realize an arbitrary unitary transformation by using the photonic path and polarization based on the existing decomposition algorithm. In the first architecture, the number of required interferometers is reduced by half compared with previous works. In the second architecture, by using the HD X gate, all the elementary operations are transferred to the operations which act locally on the photonic polarization in the same path. Such an architecture could be of significance in polarization-based applications. Both architectures maintain the symmetric layout. Our work facilitates the optical implementation of HD transformations and could have potential applications in HD quantum computation and quantum communication.

All optical comparator using tera hertz optical asymmetric demultiplexer (TOAD)

TOAD or Tera Hertz Optical Asymmetric Demultiplexer is an optical switch. Using this switch, we have invented an optical comparator. This optical comparator circuit is very simple in structure with a higher speed (200 Gbps) and finds useful application in optical computation and communication network system. We have simulated all three outputs in detail and shown the eye diagram of all three outputs of the optical comparator. We found high values of extinction ratio (ER), contrast ratio (CR), and quality factor (Q value) for the three outputs of the comparator.

Superconductor/Ferromagnet Heterostructures: A Platform for Superconducting Spintronics and Quantum Computation

AbstractThe interplay between superconductivity and ferromagnetism in the superconductor/ferromagnet (SC/FM) heterostructures generates many interesting physical phenomena, including spin‐triplet superconductivity, superconducting order parameter oscillation, and topological superconductivity. The unique physical properties make the SC/FM heterostructure as promising platforms for future superconducting spintronics and quantum computation applications. In this article, important research progress of SC/FM heterostructures from superconducting spintronics to quantum computation is reviewed, and it is organized as follows. First, the progress of spin current carriers in SC/FM heterostructures including Bogoliubov quasiparticles, superconducting vortex, and spin‐triplet Cooper pairs which might be used for long‐range spin transport is discussed. Then, the π Josephson junctions and their application for constructing π qubits are described. Finally, experimental signatures of Majorana states in the SC/FM heterostructures and the theoretically proposed manipulation are briefly reviewed, which could be useful to realize fault‐tolerant topological quantum computing.

Analisis Pemetaan Komputasi Bibliometrik Artikel Penelitian Partikel Fe2O3 Menggunakan Vosviewer

This study aims to examine the development of Fe2O3 particle research with bibliometric analysis through the application of computation using VOSviewer. The article data is sourced from Google Scholar obtained with the reference manager application, publish or perish 8. The search is based on the keyword “Fe2O3, particles, paint and coating” contained in the title and abstract. The search yielded 992 articles matching keywords with a publication range from 2012 to 2022. Based on the search results, there were 240 other related items with a total link strength of 19,323 and briefly divided into 7 clusters. The results of the analysis of engineering development for the last 10 years show results that increase every year, except in 2022. This decline occurs from 2021 with 141 published articles, and in 2022, 120 articles have been published. This is based on search data as of September 2022. , so it is still possible to occur at the end of 2022. This research can be used as a prelude to research related to Fe2O3 particles.

Topological Superconductivity Based on Antisymmetric Spin-Orbit Coupling.

Topological superconductivity (TSC) has drawn much attention for its fundamental interest and application in quantum computation. An outstanding challenge is the lack of intrinsic TSC materials with a p-wave pairing gap, which has led to the development of an effective p-wave theory of coupling s-wave gap with Rashba spin-orbit coupling (RSOC). However, the RSOC-strict mechanism and materials pose still both fundamental and practical limitations. Here, we generalize this theory to antisymmetric SOC (ASOC). Using k·p perturbation theory, we demonstrate that 2D crystals, with point groups of C2, C4, C6, C2v, C4v, C6v, D2, D4, D6, S4, or D2d, can all facilitate the desired ASOC. Remarkably, this enables us to discover 314 TSC candidates by screening 2D material databases, which are further confirmed by first-principles calculations of Majorana boundary modes and the topological invariant of the superconducting gap. Our work fundamentally enriches TSC theory and greatly expands the classes of TSC materials for experimental exploration.

Jacobians and Gradients for Cartesian Differential Categories

Cartesian differential categories come equipped with a differential combinator that formalizes the directional derivative from multivariable calculus. Cartesian differential categories provide a categorical semantics of the differential lambda-calculus and have also found applications in causal computation, incremental computation, game theory, differentiable programming, and machine learning. There has recently been a desire to provide a (coordinate-free) characterization of Jacobians and gradients in Cartesian differential categories. One's first attempt might be to consider Cartesian differential categories which are Cartesian closed, such as models of the differential lambda-calculus, and then take the curry of the derivative. Unfortunately, this approach excludes numerous important examples of Cartesian differential categories such as the category of real smooth functions. In this paper, we introduce linearly closed Cartesian differential categories, which are Cartesian differential categories that have an internal hom of linear maps, a bilinear evaluation map, and the ability to curry maps which are linear in their second argument. As such, the Jacobian of a map is defined as the curry of its derivative. Many well-known examples of Cartesian differential categories are linearly closed, such as, in particular, the category of real smooth functions. We also explain how a Cartesian closed differential category is linearly closed if and only if a certain linear idempotent on the internal hom splits. To define the gradient of a map, one must be able to define the transpose of the Jacobian, which can be done in a Cartesian reverse differential category. Thus, we define the gradient of a map to be the curry of its reverse derivative and show this equals the transpose of its Jacobian. We also explain how a linearly closed Cartesian reverse differential category is precisely a linearly closed Cartesian differential category with an appropriate notion of transpose.

Superior dark-state cooling via nonreciprocal couplings in trapped atoms

Cooling the trapped atoms toward their motional ground states is key to applications of quantum simulation and quantum computation. By utilizing nonreciprocal couplings between two atoms, we present an intriguing dark-state cooling scheme in Λ-type three-level structure, which is shown superior than the conventional electromagnetically-induced-transparency cooling in a single atom. The effective nonreciprocal couplings can be facilitated either by an atom–waveguide interface or a free-space photonic quantum link. By tailoring system parameters allowed in dark-state cooling, we identify the parameter regions of better cooling performance with an enhanced cooling rate. We further demonstrate a mapping to the dark-state sideband cooling under asymmetric laser driving fields, which shows a distinct heat transfer and promises an outperforming dark-state sideband cooling assisted by collective spin–exchange interactions.

Dynamical Behavior of Two Interacting Double Quantum Dots in 2D Materials for Feasibility of Controlled-NOT Operation.

Two interacting double quantum dots (DQDs) can be suitable candidates for operation in the applications of quantum information processing and computation. In this work, DQDs are modeled by the heterostructure of two-dimensional (2D) MoS2 having 1T-phase embedded in 2H-phase with the aim to investigate the feasibility of controlled-NOT (CNOT) gate operation with the Coulomb interaction. The Hamiltonian of the system is constructed by two models, namely the 2D electronic potential model and the matrix model whose matrix elements are computed from the approximated two-level systems interaction. The dynamics of states are carried out by the Crank–Nicolson method in the potential model and by the fourth order Runge–Kutta method in the matrix model. Model parameters are analyzed to optimize the CNOT operation feasibility and fidelity, and investigate the behaviors of DQDs in different regimes. Results from both models are in excellent agreement, indicating that the constructed matrix model can be used to simulate dynamical behaviors of two interacting DQDs with lower computational resources. For CNOT operation, the two DQD systems with the Coulomb interaction are feasible, though optimization of engineering parameters is needed to achieve optimal fidelity.

Linear-depth quantum circuits for multiqubit controlled gates

Quantum circuit depth minimization is critical for practical applications of circuit-based quantum computation. In this work, we present a systematic procedure to decompose multiqubit controlled unitary gates, which is essential in many quantum algorithms, to controlled-NOT and single-qubit gates with which the quantum circuit depth only increases linearly with the number of control qubits. Our algorithm does not require any ancillary qubits and achieves a quadratic reduction of the circuit depth against known methods. We show the advantage of our algorithm with proof-of-principle experiments on the IBM quantum cloud platform.

Toward applications of cloud quantum computation

Toward applications of cloud quantum computation

Deep optical reservoir computing and chaotic synchronization predictions based on the cascade coupled optically pumped spin-VCSELs.

In this work, we utilize two cascade coupling modes (unidirectional coupling and bidirectional coupling) to construct a four-layer deep reservoir computing (RC) system based on the cascade coupled optically-pumped spin-VCSEL. In such a system, there are double sub-reservoirs in each layer, which are formed by the chaotic x-PC and y-PC emitted by the reservoir spin-VCSEL in each layer. Under these two coupling modes, the chaotic x-PC and y-PC emitted by the driving optically-pumped spin-VCSEL (D-Spin-VCSEL), as two learning targets, are predicted by utilizing the four-layer reservoirs. In different parameter spaces, it is further explored that the outputs of the double sub-reservoirs in each layer are respectively synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL. The memory capacities (MCs) for the double sub-reservoirs in each layer are even further investigated. The results show that under two coupling modes, the predictions of the double sub-reservoirs with higher-layer for these two targets have smaller errors, denoting that the higher-layer double sub-reservoirs possess better predictive learning ability. Under the same system parameters, the outputs of the higher-layer dual parallel reservoirs are better synchronized with two chaotic PCs emitted by the D-Spin-VCSEL, respectively. The larger MCs can also be obtained by the higher-layer double reservoirs. In particular, compared with the four-layer reservoir computing system under unidirectional coupling, the four-layer reservoir computing system under bidirectional coupling shows better predictive ability in the same parameter space. The chaotic synchronizations predicted by each layer double sub-reservoirs under bidirectional coupling can be obtained higher qualities than those under unidirectional coupling. By the optimization of the system parameters, the outputs of the fourth-layer double sub-reservoirs are almost completely synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL, respectively, due to their correlation coefficient used to measure synchronization quality can be obtained as 0.99. These results have potential applications in chaotic computation, chaotic secure communication and accurate prediction of time series.

3D Sea Surface Electromagnetic Scattering Prediction Model Based on IPSO-SVR

An Improved Particle Swarm Optimization Algorithm-Support Vector Regression Machine (IPSO-SVR) prediction model is developed in this paper to predict the electromagnetic (EM) scattering coefficients of the three-dimensional (3D) sea surface for large scenes in real-time. At first, the EM scattering model of the 3D sea surface is established based on the Semi-Deterministic Facet Scattering Model (SDFSM), and the validity of SDFSM is verified by comparing with the measured data. Using the SDFSM, the data set of backscattering coefficients from 3D sea surface is generated for different polarizations as the training samples. Secondly, an improved particle swarm optimization algorithm is proposed by combining the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The combined algorithm is utilized to optimize the parameters and train the SVR to build a regression prediction model. In the end, the extrapolated prediction for backscattering coefficients of the 3D sea surface is performed. The Root Mean Square Error (RMSE) of the IPSO-SVR-based prediction model is less than 1.2 dB, and the correlation coefficients are higher than 91%. And the prediction accuracy of the PSO-SVR-based, GA-SVR-based and IPSO-SVR-based prediction models is compared. The average RMSE of the PSO-SVR-based and GA-SVR-based prediction models is 1.4241 dB and 1.6289 dB, respectively. While the average RMSE of the IPSO-SVR-based prediction model is reduced to 1.1006 dB. Besides, the average correlation coefficient of the PSO-SVR-based and GA-SVR-based prediction models is 94.36% and 93.93%, respectively. While the average correlation coefficient of the IPSO-SVR-based prediction model reached 95.12%. It demonstrated that the IPSO-SVR-based prediction model can effectively improve the prediction accuracy compared with the PSO-SVR-based and GA-SVR-based prediction models. Moreover, the simulation time of IPSO-SVR-based prediction model is significantly decreased compared with the SDFSM, and the speedup ratio is greater than 15.0. Therefore, the prediction model in this paper has practical application in the real-time computation of sea surface scattering coefficients in large scenes.