Classical Ones Research Articles

Enhancing quantum utility: Simulating large-scale quantum spin chains on superconducting quantum computers

We present the quantum simulation of the frustrated quantum spin-12 antiferromagnetic Heisenberg spin chain with competing nearest-neighbor (J1) and next-nearest-neighbor (J2) exchange interactions in the real superconducting quantum computer with qubits ranging up to 100. In particular, we implement the Hamiltonian with the next-nearest neighbor exchange interaction in conjunction with the nearest-neighbor interaction on IBM's superconducting quantum computer and carry out the time evolution of the spin chain by employing the first-order Trotterization. Furthermore, our implementation of the second-order Trotterization for the isotropic Heisenberg spin chain, involving only nearest-neighbor exchange interaction, enables precise measurement of the expectation values of staggered magnetization observable across a range of up to 100 qubits. Notably, in both cases, our approach results in a constant circuit depth in each Trotter step, independent of the number of qubits. Our demonstration of the accurate measurement of expectation values for the large-scale quantum system using superconducting quantum computers designates the quantum utility of these devices for investigating various properties of many-body quantum systems. This will be a stepping stone to achieving the quantum advantage over classical ones in simulating quantum systems before the fault tolerance quantum era. Published by the American Physical Society 2024

Third-order smoothness metric to characterize progressive addition lenses

Although second-order surface analyses, mainly mean power and cylinder maps, are commonly used to characterize the progressive addition lens (PAL) surface, recently it has been suggested that third-order variations may also have relevancy in PAL optical and visual performance. This paper proposes a third-order smoothness metric, and its associated Riemannian distance, to further characterize PAL’s surface optical performance. These metrics can provide a complementary scoring tool to those classical ones, particularly, to analyze the transition zones between far, near, intermediate, and blending zones. A method to compute these metrics is provided. This third-order smoothness metric also enables a formal definition of the PAL principal curve, namely, the curve embedded in the PAL surface, that minimizes the line path integral joining the far and near reference points weighted by the third-order smoothness metric. Finally, the paper describes a comprehensive methodology to compute such principal curves using a level-set geodesic procedure. All these ideas are put into practice with several real PAL surfaces.

A necessary conditions for optimal singular control of McKean-Vlasov stochastic differential equations driven by spatial parameters local martingale

In this paper, we focus on stochastic singular control problems involving McKean-Vlasov stochastic differential equations driven by a spatially parameterized continuous local martingale. The drift coefficient in these equations depends on the state of the solution process and its law. The control variable consists of two components: an absolutely continuous control and a singular one. Firstly, under Lipschitz conditions, we establish the existence and uniqueness of its strong solution. Next, we derive the necessary conditions for optimal singular control under the assumption that the control domain is convex. These optimality conditions differ from the classical ones in the sense that here the adjoint equation is a McKean-Vlasov backward stochastic differential equation driven by a continuous local martingale with spatial parameters. The proof of our result is based on the derivatives of the local martingale with respect to spatial parameters and the derivative of the drift coefficient with respect to the probability measure.

Optimizing quantum refrigerators with a qubit on the hot side

We study the performance of one-qubit quantum and semiclassic refrigerators with the qubit on the hot side. We obtain the cooling power and the Ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega$$\\end{document} function. We discover that the quantum refrigerators perform differently to classic ones. We also prove that even in the high-temperature limit, where the semiclassic version of the refrigerator is valid, the results of the optimization are different to those obtained by optimizing classic refrigerators.

Numerical Investigation of Heat Transfer Intensification Using Lattice Structures in Heat Exchangers

Heat exchangers make it possible to utilize energy efficiently, reducing the cost of energy production or consumption. For example, they can be used to improve the efficiency of gas turbines. Improving the efficiency of a heat exchanger directly affects the efficiency of the device for which it is used. One of the most effective ways to intensify heat exchange in a heat exchanger without a significant increase in mass-dimensional characteristics and changes in the input parameters of the flows is the introduction of turbulators into the heat exchangers. This article investigates the increase in efficiency of heat exchanger apparatuses by introducing turbulent lattice structures manufactured with the use of additive technologies into their design. The study is carried out by numerical modeling of the heat transfer process for two sections of the heat exchanger: with and without the lattice structure inside. It was found that lattice structures intensify the heat exchange by creating vortex flow structures, as well as by increasing the heat exchange area. Thus, the ratio of convection in thermal conductivity increases to 3.03 times. Also in the article, a comparative analysis of the results obtained with the results of heat transfer intensification using classical flow turbulators is carried out. According to the results of the analysis, it was determined that the investigated turbulators are more effective than classical ones, however, the pressure losses in the investigated turbulators are much higher.

Pola Konsumsi Berita dalam Era Digital: Perbandingan Preferensi Generasi Z Terhadap Sumber Berita Tradisional dan Modern

People interaction has changed along with internet development. Digital news (internet-based) is beginning to alter classical ones. The development of social media and other digital platforms is a result of the Internet development. Reading comprehension and audience skills are undoubtedly impacted by this, particularly for Generation Z because of shifting preferences for news-based-content consumption. By comparing Generation Z's preferences for classical and digital news sources, this study aims to comprehend the trends of news consumption in the digital age. This study used a quantitative approach using a descriptive methodology by employing a valid and reliable questionnaire to collect data that were then randomly distributed to 85 communication science students in semesters 2, 4, 6, and 8. The study's findings explain why communication science students do not vary significantly from other Generation Z students in terms of their preferences for news sources or how they consume them. However, since they study communication science, they are more astute when consuming news from contemporary sources that often include false information or aren't grounded in reality.

A two‐phase features extraction approach for BRB based fault diagnosis of electromechanical system

SummaryBelief rule base (BRB) is an effective nonlinear relationship modeling approach. It has been widely used in the fault diagnosis of electromechanical systems. To improve the performance of the BRB‐based diagnostic model, a two‐phase features extraction approach called CNPCA based on complex network (CN) and principal component analysis (PCA) is proposed in this paper. In the first phase, the weighted visibility graph method is applied to transform the time series data of monitored variables into complex networks. Then the statistical attributes of the constructed networks are extracted as the initial features. In the second phase, the PCA method is used to process the initial features and the principal component features are obtained. After that, the CNPCA‐BRB diagnostic model for the electromechanical system is constructed. The experimental results of the elevator fault diagnosis show that the constructed diagnostic model outperforms better than the classical ones. It demonstrates that the CNPCA approach can ensure the integrity of fault information in the features and improve the separability of the fault features.

Exact dispersion relation of the quantum Langmuir wave.

The normal modes, i.e., the eigensolutions to the dispersion relation equation, are the most fundamental properties of a plasma. The real part indicates the intrinsic oscillation frequency while the imaginary part the Landau damping rate. In most of the literature, the normal modes of quantum plasmas are obtained by means of small damping approximation, which is invalid for high-k modes. In this paper, we solve the exact dispersion relations via the analytical continuation scheme, and, due to the multi-value nature of the Fermi-Dirac distribution, reformation of the complex Riemann surface is required. It is found that the topological shape of the root locus in quantum plasmas is quite different from classical ones, in which both real and imaginary frequencies of high-k modes increase with k steeper than the typical linear behavior in classical plasmas. As a result, the time-evolving behavior of a high-k initial perturbation becomes ballistic-like in quantum plasmas.

Quench dynamics in lattices above one dimension: The free fermionic case

We begin a systematic investigation of quench dynamics in higher-dimensional lattice systems considering the case of noninteracting fermions with conserved particle number. We prepare the system in a translational-invariant nonequilibrium initial state, the simplest example being a classical configuration with fermions at fixed positions on the lattice, and let it evolve in time. We characterize the system's dynamics by measuring the entanglement between a finite connected region and its complement. We observe the transmutation of entanglement entropy into thermodynamic entropy and investigate how this process depends on the shape and orientation of the region with respect to the underlying lattice. Interestingly, we find that irregular regions display a distinctive multislope entanglement growth, while the dependence on the orientation angle is generically fairly weak. This is particularly true for regions with a large (discrete) rotational symmetry group. The main tool of our analysis is the celebrated quasiparticle picture of Calabrese and Cardy, which we generalize to describe the case at hand. Specifically, we show that for generic initial configurations (even when restricting to classical ones) one has to allow for the production of multiplets involving n>2 quasiparticles and carrying nondiagonal correlations. We obtain quantitatively accurate predictions, tested against exact numerics, and propose an efficient Monte Carlo based scheme to evaluate them for arbitrary connected regions of generic higher-dimensional lattices. Published by the American Physical Society 2024

Resignifying pedagogical renewal today: Pedagogical benchmarks and singularities in secondary schools

Understanding what is meant by pedagogical renewal today, based on the transformative processes that are being carried out, poses a challenge, one that is even greater if the aim is to elucidate the pedagogical benchmarks and hallmarks of secondary schools. This article, which takes the form of a multiple case study, aims to bring to the fore those unique and defining elements of the selected schools, considered renewal schools, from five Spanish regions. During the fieldwork, semi-structured interviews, in-depth interviews and discussion groups were held with members of the management teams, teachers, families and students. The analysed results show that the elements highlighted by the schools linked to pedagogical renewal are unique features of them, with the school project being the tool used to construct the educational meaning of of its practices, aiming to ensure that these practices form a coherent whole with its discourse. Thus, for example, it highlights the central role of students in decision-making in their learning process or reflection with the teaching team in improving educational practice. It should be noted that they do not define themselves with the same term, and they add more current and recent pedagogical benchmarks to the classic ones. In short, this study seeks to provide a novel contribution to the study of contemporary pedagogical renewal, given the lack of such studies in secondary school education.

TriChronoNet: Advancing electricity price prediction with Multi-module fusion

This study introduces a novel architecture for electricity price forecasting, comprising four modules designed for prediction and information fusion. Three modules are dedicated to preliminary price prediction, while the fourth integrates information from prior predictions to generate final forecasts. Experimental evaluations demonstrate the effectiveness of the proposed model, showcasing superior performance compared to models from classical ones to cutting-edge ones in time-series modeling. Specifically, results show improvements of 3.51%–53.09% on RMSE, and 4.77%–59.19% on MAE. Additionally, we conduct an ablation study to analyze the robustness of the proposed model and the distinct contributions of its modules. The findings highlight the different roles of each component and provide valuable insights for future research in electricity price prediction. Given the critical role of accurate electricity price prediction in promoting the efficiency of electricity trading and market health, this method offers a promising avenue for advancing prediction techniques in this domain.

Exact Quantum Sensing Limits for Bosonic Dephasing Channels

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges toward realizing useful quantum computation, communication, and sensing. Here, we consider discrimination and estimation of bosonic dephasing channels, when using the most general adaptive strategies allowed by quantum mechanics. We reduce these difficult quantum problems to simple classical ones based on the probability densities defining the bosonic dephasing channels. By doing so, we rigorously establish the optimal performance of various distinguishability and estimation tasks and construct explicit strategies to achieve this performance. To the best of our knowledge, this is the first example of a non-Gaussian bosonic channel for which there are exact solutions for these tasks. Published by the American Physical Society 2024

Theoretical Justification for the Implementation of Special Economic Zones: The Case of Luanda-Bengo Economic Zone in Angola

Purpose: In the present study, a literature review was carried out on theories of regional development, namely: the classic ones, those with an emphasis on agglomeration factors, and new theories of regional development. It intended to provide a theoretical foundation for implementing special economic zones, contextualizing the analysis to the Luanda-Bengo Special Economic Zone. Materials and Methods: Thus, the document analysis technique was used to review relevant literature on regional development and its main supporting theories. Findings: It was possible to achieve three main results: that the existence of hubs that bring together population and capital play a central role in the location of commercial and industrial activities; that companies come together in a logic of cooperation, innovation competitiveness, and ZEELB, as a hub of industrial activity, is no exception. Finally, the factors that determine the location and spatial agglomeration of economic activities, as well as the relationships of interdependence established between the various driving and driven units, possible in a ZEE, are also present in the foundations of adopting ZEELB operations. Implications to Theory, Practice and Policy: Theories about the location and agglomeration of economic activities in geographically, customs, and legally demarcated spaces, such as ZEELB, should be used in studies for future implementations of similar projects. Exchange between academia and structuring investment planning bodies such as ZEELB, so that based on its adoption as part of the global economic development strategy, is necessary. Public policymakers should see ZEELB as an opportunity to serve as a laboratory for policies at the following levels: tax, customs, regulatory, commercial, and management

TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph Reasoning

Knowledge Graph (KG) Reasoning plays a vital role in various applications by predicting missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph Neural Networks (GNNs) have shown impressive performance, particularly when reasoning with unseen entities and dynamic KGs. However, such state-of-the-art KG reasoning approaches encounter efficiency and scalability challenges on large-scale KGs due to the high computational costs associated with subgraph extraction - a key component in inductive KG reasoning. To address the computational challenge, we introduce TIGER, an inductive GNN training framework tailored for large-scale KG reasoning. TIGER employs a novel, efficient streaming procedure that facilitates rapid subgraph slicing and dynamic subgraph caching to minimize the cost of subgraph extraction. The fundamental challenge in TIGER lies in the optimal subgraph slicing problem, which we prove to be NP-hard. We propose a novel two-stage algorithm SiGMa to solve the problem practically. By decoupling the complicated problem into two classical ones, SiGMa achieves low computational complexity and high slice reuse. We also propose four new benchmarks for robust evaluation of large-scale inductive KG reasoning, the biggest of which performs on the Freebase KG (encompassing 86M entities, 285M edges). Through comprehensive experiments on state-of-the-art GNN-based KG reasoning models, we demonstrate that TIGER significantly reduces the running time of subgraph extraction, achieving an average 3.7× speedup relative to the basic training procedure.

Glass Fibre-Reinforced Composite Materials Used in the Aeronautical Transport Sector: A Critical Circular Economy Point of View

Progress in composite materials has led to their use in applications where improved mechanical and resistance characteristics are required. Most composites are obtained in such a way that they present specific mechanical properties and/or have the role of both a thermal conductor and insulator; these properties are important, specific, specialized, and useful. The advantages of these materials compared to the classic ones are as follows: low weight, high resistance to wear and corrosion, and mechanical characteristics consistent with the subsequent use of the product. The slightly high costs of these materials are justified by their precision, the quality of the products obtained, and the fact that their use leads to increased reliability, maintenance, and, in the cases of the automotive and aeronautical industries, reduced energy consumption. This paper aims to bring to readers’ attention the latest research related to glass fibre-reinforced composite materials in transport-related applications, such as automotive and aeronautic applications, including the manufacturing of unmanned aerial vehicles (UAVs). Considering the long period of use, the recycling and reuse of composite materials used in aeronautical transport is a must considering the environmental aspects and the need of achieving a circular economy. In recent years, considerable efforts have been made to find new alternatives to improve the performance and durability of materials in the aeronautical transport sector.

A modified Lorentz force law for point-like charged particles in classical electrodynamics

In classical electrodynamics, the well-known Lorentz force law falls short of providing a satisfactory result for the trajectory of point-like charged particles when considering that particle’s own self-force. While there have been many historical attempts, Gralla, Harte and Wald developed a new model for classical charged particles that is free from pathologies while being consistent with Maxwell’s equations and conserves stress-energy. Expanding upon this approach, we derive a relativistically correct, modified Lorentz force law in vector form, which includes radiation reaction, and spin- and magnetic moment-dependent correction terms, suitable to be included in classical electrodynamics lectures and beam dynamics simulation tools. As by-products we obtain evolution equations for mass, spin angular momentum and the radiated power. We compare the new equations to the classical ones and use the new equations to conduct numerical simulations, showing that the results are free of any nonphysical artifacts, and which might be possible to test in future experiments at particle accelerators. The new equations foster improved insight into beam dynamics.

SincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

THE ROLE OF ADMINISTRATIVE CONTRACTS IN THE FIELD OF PUBLIC ADMINISTRATION

Background: This scientific paper aims to delve deeply into the concept of administrative contracts and their importance in the field of public administration. Therefore, our goal is to provide a clear and detailed analysis and interpretation for readers, ensuring that all those interested have the opportunity to gain a foundational understanding of the importance and legal consequences of administrative contracts. By means of this paper, treating administrative contracts broadly regarding their development, meaning and importance in the theoretical aspect will positively influence and facilitate their application in practice by the public administration. These contracts, often similar to classical ones, with their content and purpose, are so differentiated that now it is no longer possible to speak of their belonging to one of the existing groups of contracts but of new, independent types of contracts. Methods: This paper employs analytical, normative, historical and comparative methods. The analytical method will be used to analyse administrative contracts in the Republic of Kosovo and their application by state bodies. The normative method will treat the legal provisions that regulate administrative contracts, starting with those within administrative law and extending to provisions in other legal domains. The historical method will illustrate the history of the development of administrative contracts, detailing their past and how they work today. Lastly, the comparative method will compare the development and operation of administrative contracts in the Republic of Kosovo with those in other democratic states mentioned in the paper. Results and conclusions: The administrative contract holds significant importance in public administration, as its primary objective is always to serve the general state interest. Despite being a bilateral legal act, an administrative contract typically involves a public or state administration body as the contracting party, which inherently holds greater power or authority in relation to the other legal entity involved. This power disparity means there is no equal footing between the contracting parties, contrary to the principle of equality observed in civil law and generally required for concluding private contracts.

An algorithm based on quantum phase estimation for the identification of patterns

The quantum phase estimation algorithm has been utilized by a variety of quantum algorithms, most notably Shor’s algorithm, to obtain information regarding the period of a function that is appropriately encoded into a unitary operator. In many cases, it is desired to estimate whether a specific state exhibits a certain pattern quickly. In this paper, we exhibit a methodology based on the QPE algorithm to identify certain patterns. In particular, starting from a properly encoded state, we demonstrate how to construct unitary operators whose eigenvectors correspond to states with proper diagonals. QPE will then output an eigenphase equal to zero with certainty for these states, thereby identifying this class of matrices. For partial matches, our algorithm, based on the tolerance threshold used, will show areas of high similarity, and it will outperform classical ones when the number of mismatches defined by the tolerance is significantly low when compared to the size of the diagonal.

Quadratic acceleration of multistep probabilistic algorithms for state preparation

Quantum state preparation is a fundamental building block for various problems on a quantum computer. A nonunitary operator is typically designed to decay unwanted states contained in an initial state using ancilla qubits and a probabilistically action. In this Letter, we clarify that this probabilistic nature is a drag for quantum advantages: The probabilistic algorithms do not accelerate the computational process compared to classical ones. Combining quantum amplitude amplification (QAA) with multistep probabilistic algorithms is proposed to address this drawback, achieving quadratic acceleration and quantum advantages. We also find that the multistep probabilistic method with QAA exhibits advantages over quantum phase estimation in terms of infidelity. We also demonstrate it to confirm the quadratic acceleration, using a probabilistic imaginary-time evolution method as an example. Published by the American Physical Society 2024