Step Of Algorithm Research Articles

Physics-informed deep learning with Kalman filter mixture for traffic state prediction

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model called the Physics Informed-Graph Convolutional Gated Recurrent Neural Network (PI-GRNN). Functioning as the state-space model within the KF, the PI-GRNN exploits established correlations to construct dynamic adjacency matrices that utilize the inherent structure and relationships within the transportation network to capture sequential patterns and dependencies over time. Furthermore, our methodology integrates insights gained from correlations into the correction step of the KF algorithm that helps in enhancing its correctional capabilities. This integrated approach proves instrumental in alleviating the inherent model drift associated with data-driven methods, as periodic corrections through update step of KF refine the predictions generated by PI-GRNN. To the best of our knowledge, this study represents a pioneering effort in integrating deep learning and KF algorithms in this unique symbiotic manner. Through extensive experimentation with real-world traffic data, we demonstrate the superior performance of our model compared to the benchmark approaches.

An adaptive fluid-solid coupling time step control algorithm for unsteady conjugate heat transfer

Unsteady simulation with conjugate heat transfer has to tackle the stiffness issue that originates from the wide disparate time scales between fluid and solid heat transfer. In the loosely coupled framework, challenges remain in determining the optimal coupling time step between the fluid and solid solvers, in terms of efficiency and accuracy. This study formulates a correlation for the coupling error, together with the Proportional-Integral-Derivative (PID) control method, to propose an adaptive fluid-solid coupling time step algorithm, which dynamically adjusts the coupling time step such that the computational efficiency can be improved without sacrificing accuracy. The proposed algorithm has been tested in a 1D conjugate heat transfer application with typical operating conditions of a real engine, and the performance of different controllers (I, PI, PID) has been analyzed and compared. Results show that with the closed-loop control, all controllers can ensure the error control below the user specified tolerance at the cost of reducing efficiency. Meanwhile, with the open-loop control which is of more interest to practical applications, controllers suffer from instability issues when a conventional algorithm based on integration error is employed. The proposed coupling-error-based algorithm successfully tackles the instability issues. Compared with the baseline algorithm of the constant coupling time step, the proposed algorithm can reduce approximately 40%–90% computational cost for the 1D cases considered.

NN-Steiner: A Mixed Neural-Algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem

Recent years have witnessed rapid advances in the use of neural networks to solve combinatorial optimization problems. Nevertheless, designing the "right" neural model that can effectively handle a given optimization problem can be challenging, and often there is no theoretical understanding or justification of the resulting neural model. In this paper, we focus on the rectilinear Steiner minimum tree (RSMT) problem, which is of critical importance in IC layout design and as a result has attracted numerous heuristic approaches in the VLSI literature. Our contributions are two-fold. On the methodology front, we propose NN-Steiner which is a novel mixed neural-algorithmic framework for computing RSMTs that leverages the celebrated PTAS algorithmic framework of Arora to solve this problem (and other geometric optimization problems). Our NN-Steiner replaces key algorithmic components within Arora's PTAS by suitable neural components. In particular, NN-Steiner only needs four neural network (NN) components that are called repeatedly within an algorithmic framework. Crucially, each of the four NN components is only of bounded size independent of input size, and thus easy to train. Furthermore, as the NN component is learning a generic algorithmic step, once learned, the resulting mixed neural-algorithmic framework generalizes to much larger instances not seen in training. Our NN-Steiner, to our best knowledge, is the first neural architecture of bounded size that has capacity to approximately solve RSMT (and variants). On the empirical front, we show how NN-Steiner can be implemented and demonstrate the effectiveness of our resulting approach, especially in terms of generalization, by comparing with state-of-the-art methods (both neural and non-neural based).

Efficient quantum amplitude encoding of polynomial functions

Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on n qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation (MPS). We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers either via its MPS or with a shallow sequence of multi-controlled gates that loads the linear function's Hadamard-Walsh series, and we explore how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform converts the state encoding the series coefficients into an amplitude encoding of the linear function. Thus, we use this construction as a building block to achieve an exact block encoding of the amplitudes corresponding to the linear function on k0 qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on k0 qubits. Finally we pad n−k0 qubits to generate an approximated encoding of the polynomial on n qubits, analyzing the error depending on k0. In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors.

A novel SCDM algorithm with offset centroid-driven weight adaptation and its application to appearance design of automotive steering wheels

This paper introduces a novel group single-criteria decision-making algorithm that addresses the challenges from personal biases. The algorithm utilizes offset centroid-driven weight adaptation to enhance the fairness and reliability of decisions, providing robust decision-making. The core steps of the new algorithm include splitting the evaluation dataset, calculating the offset centroid, introducing dynamic adjustment coefficients, constructing a comparison matrix, and calculating the weighted mean. To empirically validate the effectiveness and applicability of the proposed algorithm, we conducted a case study involving five appearance design alternatives for automotive steering wheels, and the numerical results demonstrate the algorithm’s substantial improvements on group decision-making outcomes. Remarkably, the algorithm not only facilitates the flourishing of a fair decision-making environment but also can effectively handles biased decision-making scenarios and mitigate the impact of unfairness. By utilizing this algorithm, decision-makers can alleviate individual biases and enable fairer and more reliable decision-making processes. Consequently, this algorithm introduces a novel approach for tackling complex decision problems and exhibits promising prospects for practical applications. Its versatility renders it highly valuable in diverse decision-making processes, empowering decision-makers to achieve fairness and precision in their choices.

The Application of Mathematical Modelling to Optimize Steel Reinforcement Shipping Cost for Construction Projects by Julius Berger

The problem of transporting steel reinforcement for construction purposes has always been eminent in construction projects. In this study, the transportation model was applied while shipping steel reinforcement from three supply locations to three construction sites owned by Julius Berger construction limited, in the city of Port Harcourt. The study was done for 100-ton truck load of steel reinforcement bars with standard 12m lengths and diameters 8mm to 40mm. This was from the three selected steel depots in Port Harcourt. The costs of transporting the steel reinforcement bars were analyzed using the basic steps of simplex algorithm for solving transportation problems. The initial and optimum feasible solutions were obtained. The Stepping Stone method was used for the optimum solution after three consecutive iterations, which amounted to $3,813 or (₦2,897,880 as of March 2023). The result was compared with that obtained from Microsoft Excel solver. There was no difference between the two results.

Ethical consumption and food recovery hierarchy behaviors: a clustering analysis in Japan

AbstractConsumption and waste disposal decisions can have a profound impact on the environment. The present research is focused on understanding the adoption profiles of pro-environmental behaviors (PEB) within the Japanese population. The scope of the evaluated PEB covered the purchase, usage, and disposal of food products. Ethical consumption behaviors (the purchase of little packaging, organic, eco-friendly and/or refillable goods), food waste prevention actions (leftovers avoidance, meal planning, and food waste reduction/avoidance), and alternative disposal measures (feeding animals, composting, giving food to neighbors) were analyzed. The profiles were found via a two-step clustering technique using self-reported data from a survey done in nine Japanese prefectures. The first step of the clustering algorithm comprised a hierarchical technique, and the second was a K-means with an Euclidean distance. Four significantly distinct clusters were found, namely: sporadic adopters, average practitioners, responsible consumers, and environmentally involved. Approximately 51% of the sample was allocated to the responsible consumers cluster, which was characterized by having more women in part-time jobs or in-house duties and older respondents than the other groups. Environmentally involved tended to be young respondents with children and gardening/farming interest. They were also the ones who ate plant-based meals more frequently and had healthier eating habits.

Upgrade of the ATLAS Level-0 TGC Endcap Muon Trigger for HL-LHC

The Level-0 endcap muon trigger system for the ATLAS experiment at the HL-LHC, which performs a fast muon reconstruction using the Thin Gap Chamber (TGC), is being developed to cope with more than three times higher luminosity environment and achieve a better performance. The efficiency in the high-p T region was found to be 9% higher than the current system. The TGC hit signals digitized at the frontend electronics are sent to the backend, and muon candidates are reconstructed using the hits. Then, the candidates are combined with information from other detectors to refine transverse momentum (p T) reconstruction and reduce fake trigger candidates. The precise online estimation of p T allows us to apply highly efficient trigger condition to select events that contains muons with high p T.The Sector Logic (SL) is responsible for the online fast reconstruction in the L0 Muon Trigger system, and the algorithms are implemented in a large-scale modern FPGA on the SL. Each step of the reconstruction algorithms is first implemented as individual modules, and all trigger algorithms run sequentially as a chain in a combined firmware. The trigger algorithms are being tested on the prototype trigger board, the SL. The resource usage of the FPGA is reasonable and the trigger latency is within the acceptable limit.

Artificial intelligence-based Karawo motif formation using genetic algorithm

This research explores the application of artificial intelligence in generating Karawo motifs, a traditional Indonesian pattern. The research involves collecting a dataset of existing Karawo motifs and utilizing genetic algorithms to evolve and create novel pattern variations. The generated motifs are evaluated based on their adherence to traditional design principles and aesthetic appeal. The formation of Karawo motifs begins with randomly selecting image data from a database. Then, the selection of transformation treatments is performed by optimizing the fitness function within the genetic algorithm. The applied types of transformations include geometric transformations, Boolean transformations, and arithmetic transformations. The outlined genetic algorithm steps include determining the fitness function, performing its evaluation, selecting fitness values, applying crossover, implementing mutation, managing survivor selection, and terminating iterations. The results indicate that the developed system is capable of creating diverse and appealing Karawo motif patterns, showcasing the potential of combining traditional artistry with artificial intelligence. This study has the potential to expand the possibilities of Karawo motif design and contribute to the preservation and promotion of Indonesian cultural heritage.

Nonlinear Optimal Control for a PMLSG-VSC Wave Energy Conversion Unit

This article aims to treat the nonlinear control problem for the complex dynamics of a wave energy unit (WEC) that consists of a Permanent Magnet Linear Synchronous Generator (PMLSG) and a Voltage Source Converter (VSC). The article has developed a globally stable nonlinear optimal control method for this wave power generation unit. The new method avoids complicated state-space model transformations and minimizes the energy dispersion by the control loop. A novel nonlinear optimal control method is proposed for the dynamic model of a wave energy conversion system, which includes a Permanent Magnet Linear Synchronous Generator (PMLSG) serially connected with an AC/DC three-phase voltage source converter (VSC). The dynamic model of this renewable energy system is formulated and differential flatness properties are proven about it. To apply the proposed nonlinear optimal control, the state-space model of the PMLSG-VSC wave energy conversion unit undergoes an approximate linearization process at each sampling instance. The linearization procedure relies on a first-order Taylor-series expansion and involves the computation of the system’s Jacobian matrices. It takes place at each sampling interval around a temporary operating point, which is defined by the present value of the wave energy conversion unit’s state vector and by the last sampled value of the control inputs vector. An H-infinity feedback controller is designed for the linearized model of the wave energy conversion unit. To compute the feedback gains of this controller, an algebraic Riccati equation is repetitively solved at each time step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis.

A219 EARLY CANADIAN EXPERIENCE WITH BALLOON CRYOTHERAPY FOR BARRETT’S ESOPHAGUS

Abstract Background Balloon Cryotherapy (bCr) is a novel endoscopic treatment modality for Barrett’s esophagus (BE) associated dysplasia. Cryotherapy offers an additional approach, particularly in cases refractory to or with an anatomical impediment to radiofrequency ablation (RFA), currently first line in endoscopic eradiation therapy (EET). The use of bCr is new in Canada and it was introduced in Alberta in February 2023. Aims To present the early experience of bCr use for BE from a provincial Canadian cohort. Methods An Alberta health technology assessment approved bCr, which was then incorporated into the provincial BE treatment algorithm. Indications for bCr include: 1) RFA-refractory, 2) anatomical impediment to RFA, 3) need for concomitant biopsy/EMR, and 4) pain with RFA. All patients who underwent bCr for BE in Alberta from February to September 2023 at two tertiary care centers were included. Data on demographics, BE lesion characteristics, prior BE treatments, and bCr use are presented. Results During the 8-month period, 22 patients underwent a total of 32 bCr treatments. The median age was 68, with 77% of patients being male. At baseline, 81% had long-segment BE (≥3 cm), with a median maximal BE length of 7cm. The majority had baseline histology of high-grade dysplasia (HGD) (n=11, 50%), followed by low-grade dysplasia (LGD) (n=5, 22%), T1a (n=4, 18%), T1b (n=1, 4.5%), and intestinal metaplasia (IM) (n=1, 4.5%). All patients had previously received EET, with 77% undergoing a combination of EMR, RFA, APC, and/or ESD. Most had received RFA (n=18, 81%, median sessions=5) and EMR (n=13, 59%, median sessions=2). Some had prior APC (n=10, 45%, median sessions=1) and one had ESD. Prior to bCr, most patients had converted to short-segment (ampersand:003C3cm) BE (n=16, 72%), and histology was predominately IM (31%), followed by visible BE though no biopsy (27%), HGD (22%), T1a (13%), and LGD (4.5%). The primary indication for bCr was RFA-refractory BE (n=17, 77%), followed by anatomical impediment to RFA (n=4, 18%), and concomitant EMR (n=1, 4%). No procedures were aborted due to technical difficulties and there were no major adverse events such as perforation, bleeding, or pain requiring hospitalization. Among those with follow-up after a bCr session (n=19, 59%), one reported pain, two reported self-limited dysphagia, and one had a stricture requiring dilation. Of the 7 patients with post-bCr endoscopy, all had no dysplasia on the first set of biopsies (no IM, n=4; IM, n=2; indefinite for dysplasia, n=1). Conclusions Our early experience with bCr demonstrates there is a need for additional EET for BE. Cryotherapy, in accordance with gastroenterology society guidelines, is a promising next step in the BE algorithm. In our provincial cohort, bCr has shown technical success, and preliminary data suggests that it is safe and effective in ablating resistant BE segments. Funding Agencies None

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters

Various methods have been proposed in the literature for identifying subgoals in discrete reinforcement learning (RL) tasks. Once subgoals are discovered, task decomposition methods can be employed to improve the learning performance of agents. In this study, we classify prominent subgoal identification methods for discrete RL tasks in the literature into the following three categories: graph-based, statistics-based, and multi-instance learning (MIL)-based. As contributions, firstly, we introduce a new MIL-based subgoal identification algorithm called EMDD-RL and experimentally compare it with a previous MIL-based method. The previous approach adapts MIL’s Diverse Density (DD) algorithm, whereas our method considers Expected-Maximization Diverse Density (EMDD). The advantage of EMDD over DD is that it can yield more accurate results with less computation demand thanks to the expectation-maximization algorithm. EMDD-RL modifies some of the algorithmic steps of EMDD to identify subgoals in discrete RL problems. Secondly, we evaluate the methods in several RL tasks for the hyperparameter tuning overhead they incur. Thirdly, we propose a new RL problem called key-room and compare the methods for their subgoal identification performances in this new task. Experiment results show that MIL-based subgoal identification methods could be preferred to the algorithms of the other two categories in practice.

An Improved MP Algorithm for Power System Mode Identification

An improved matrix pencil (MP) algorithm for power system mode identification has been proposed in this paper. Aiming at the problem of inaccurate order determined by the singular value decomposition (SVD) step in traditional MP algorithms and the lack of Hankel structure in the reconstructed matrix, which leads to inaccurate identification under low signal-to-noise ratio (SNR) condition, singular entropy order determination is introduced. An iterative algorithm is proposed to calculate a reconstructed matrix with both Hankel structure and rank-deficient attributes. This reconstruction matrix has a better approximation effect and improves the identification accuracy of the MP algorithm under low SNR. The effectiveness of the proposed algorithm was verified by modal parameter identification of numerical signals and oscillation signals of the 4-machine and 2-area systems. The algorithm proposed in low SNR has higher accuracy than the traditional MP algorithm, and the reconstructed signal has a better fitting effect.

Prediction of cetane number of biodiesel from its fatty acid ester composition using Artificial Neural Networks

Models for estimation of the cetane number of biodiesel from their methyl ester composition using artificial neural networks were obtained in this work. An experimental data that covers 48 and 15 biodiesels in the modeling step and validation step respectively were taken. The selection of biodiesel samples took into account a wide range of ester compounds with different unsaturation characteristics and number of carbon atoms. A model to predict cetane number using artificial neural networks was obtained with better accuracy than 95 %. The best neural network for predict the cetane number was a backpropagationnetwork(11:4:1)using a Conjugate Gradient Descend algorithm for the second training step and showing 96.3 % of correlation for the validation data and a mean absolute error of 1.5.The proposed network is useful for prediction of the cetane number of biodiesel in a wide range of composition but keeping the percent of total unsaturations lower than 80 %. The use of the artificial neural networks in this case let to study and understands the effect of individual fatty acids in the cetane number. Therefore they can be used for improving some biofuel properties related to the combustion process and its efficiency.

MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)

Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (ResNeXt) and squeeze-and-excitation mechanisms into the ISTA block. This block serves as a deep equilibrium layer connected to a semi-tensor product network for convenient sampling and providing an initial reconstruction. The resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods. It significantly reduces storage requirements compared to deep unrolling methods, using only one iteration block instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can be iteratively used, gradually improving reconstruction accuracy while considering computation tradeoffs. Additionally, the model benefits from multiscale dilated convolutions, further enhancing performance.

Machine learning approaches for automatic classification of single-particle mass spectrometry data

Abstract. The chemical composition of aerosol particles is a key parameter for human health and climate effects. Single-particle mass spectrometry (SPMS) has evolved to a mature technology with unique chemical coverage and the capability to analyze the distribution of aerosol components in the particle ensemble in real time. With the fully automated characterization of the chemical profile of the aerosol particles, selective real-time monitoring of air quality could be performed, e.g., for urgent risk assessments due to particularly harmful pollutants. For aerosol particle classification, mostly unsupervised clustering algorithms (ART-2a, K-means and their derivatives) are used, which require manual postprocessing. In this work, we focus on supervised algorithms to tackle the problem of the automatic classification of large amounts of aerosol particle data. Supervised learning requires data with labels to train a predictive model. Therefore, we created a labeled benchmark dataset containing ∼ 24 000 particles with eight different coarse categories that were highly abundant at a measurement in summer in Central Europe: elemental carbon (EC), organic carbon and elemental carbon (OC-EC), potassium-rich (K-rich), calcium-rich (Ca-rich), iron-rich (Fe-rich), vanadium-rich (V-rich), magnesium-rich (Mg-rich) and sodium-rich (Na-rich). Using the chemical features of particles, the performance of the following classical supervised algorithms was tested: K-nearest neighbors, support vector machine, decision tree, random forest and multi-layer perceptron. This work shows that despite the entrenched position of unsupervised clustering algorithms in the field, the use of supervised algorithms has the potential to replace the manual step of clustering algorithms in many applications, where real-time data analysis is essential. For the classification of the eight classes, the prediction accuracy of several supervised algorithms exceeded 97 %. The trained model was used to classify ∼ 49 000 particles from a blind dataset in 0.2 s, taking into account also a class of “unclassified” particles. The predictions are highly consistent with the results obtained in a previous study using ART-2a.

Solution of First Order Ordinary Differential Equations Using Fourth Order Runge-Kutta Method with MATLAB.

Differential Equations are used in developing models in the physical sciences, engineering, mathematics, social science, environmental sciences, medical sciences and other numerous fields. This article examined solution of first ordinary differential equation using fourth order Runge-Kutta method with MATLAB. The fourth order Runge-Kutta method for modelling differential equations improves upon the Euler’s method to obtain a greater accuracy without the necessity for higher-order derivatives of the given function. A first order differential equation was solved using fourth order Runge-Kutta method with MATLAB and the same problem was solved analytically in order to obtain the exact solution. The MATLAB commands match up quickly with the steps of the fourth order Runge-Kutta algorithm. Slight variation of the MATLAB code was used to show the effect of the size of h on the accuracy of the solution (see figure 4.1, 4.2, 4.3).The MATLAB and exact solutions are approximately equal though the MATLAB approach is easier and faster. The obtained results are in agreement with those in existing literature and improved the results obtained by [1]

Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control

Ultracold polar molecules have gained much attention as a potential candidate for quantum information processing due to their long coherence time and strong dipole–dipole interaction. In the present study, we explore the dynamics of three polar molecules that are mutually coupled and arranged in an equilateral triangle configuration within a gradient electric field. By employing the pendular states of polar molecules as qubits, we simulate the three-qubit Deutsch-Jozsa algorithm via multi-target optimal control. Through the design of optimal microwave pulses with multiple iterations, our approach can accomplish the operations required to complete the algorithm in a single operational step with high fidelity and large transition probability. Furthermore, we compare and analyze the fidelity and average transition probability with respect to iteration numbers for each unitary operation Uf pulse. It is found that by combining the optimized X, H⊗H⊗H, Uf, and H⊗H gate pulses, the Deutsch-Jozsa algorithm in the three-dipole system can be performed well for distinguishing between constant and balanced functions. Our findings could shed some light on the physical realization of multi-particle quantum algorithm based on polar molecules in external electric fields.

Mengidentifikasi Dasar Algoritma Pemrograman

Programming algorithms are the basic principles when creating programs on a computer. Although closely related to mathematical calculations, real programming algorithms do not always involve complex calculations such as genetic algorithms which are usually used to solve mathematical problems. In analyzing an algorithm there are several parts that we can analyze, such as time speed, amount of cost, and amount of space. The 3 parts each use O (big O) notation. There is also Divided and Conquer. In this article the author uses a method, namely literature study. Below the author will present the basic steps of the programming algorithm