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Journal articles on the topic 'Computational Advantage'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Zhong, Han-Sen, Hui Wang, Yu-Hao Deng, et al. "Quantum computational advantage using photons." Science 370, no. 6523 (2020): 1460–63. http://dx.doi.org/10.1126/science.abe8770.

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Madsen, Lars S., Fabian Laudenbach, Mohsen Falamarzi Askarani, et al. "Quantum computational advantage with a programmable photonic processor." Nature 606, no. 7912 (2022): 75–81. http://dx.doi.org/10.1038/s41586-022-04725-x.

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AbstractA quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quant

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May, Mike. "Computational Tools Take Advantage of the Data Deluge." Genetic Engineering & Biotechnology News 43, no. 4 (2023): 42–44. http://dx.doi.org/10.1089/gen.43.04.14.

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Bravyi, Sergey, David Gosset, and Robert König. "Quantum advantage with shallow circuits." Science 362, no. 6412 (2018): 308–11. http://dx.doi.org/10.1126/science.aar3106.

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Quantum effects can enhance information-processing capabilities and speed up the solution of certain computational problems. Whether a quantum advantage can be rigorously proven in some setting or demonstrated experimentally using near-term devices is the subject of active debate. We show that parallel quantum algorithms running in a constant time period are strictly more powerful than their classical counterparts; they are provably better at solving certain linear algebra problems associated with binary quadratic forms. Our work gives an unconditional proof of a computational quantum advantag

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Kendon, Viv, Angelika Sebald, and Susan Stepney. "Heterotic computing: past, present and future." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (2015): 20140225. http://dx.doi.org/10.1098/rsta.2014.0225.

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We introduce and define ‘heterotic computing’ as a combination of two or more computational systems such that they provide an advantage over either substrate used separately. This first requires a definition of physical computation. We take the framework in Horsman et al. (Horsman et al. 2014 Proc. R. Soc. A 470, 20140182. ( doi:10.1098/rspa.2014.0182 )), now known as abstract-representation theory, then outline how to compose such computational systems. We use examples to illustrate the ubiquity of heterotic computing, and to discuss the issues raised when one or more of the substrates is not

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Oliveira, Michael de, Luís S. Barbosa, and Ernesto F. Galvão. "Quantum advantage in temporally flat measurement-based quantum computation." Quantum 8 (April 9, 2024): 1312. http://dx.doi.org/10.22331/q-2024-04-09-1312.

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Several classes of quantum circuits have been shown to provide a quantum computational advantage under certain assumptions. The study of ever more restricted classes of quantum circuits capable of quantum advantage is motivated by possible simplifications in experimental demonstrations. In this paper we study the efficiency of measurement-based quantum computation with a completely flat temporal ordering of measurements. We propose new constructions for the deterministic computation of arbitrary Boolean functions, drawing on correlations present in multi-qubit Greenberger, Horne, and Zeilinger

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Yoganathan, Mithuna, Richard Jozsa, and Sergii Strelchuk. "Quantum advantage of unitary Clifford circuits with magic state inputs." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2225 (2019): 20180427. http://dx.doi.org/10.1098/rspa.2018.0427.

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We study the computational power of unitary Clifford circuits with solely magic state inputs (CM circuits), supplemented by classical efficient computation. We show that CM circuits are hard to classically simulate up to multiplicative error (assuming polynomial hierarchy non-collapse), and also up to additive error under plausible average-case hardness conjectures. Unlike other such known classes, a broad variety of possible conjectures apply. Along the way, we give an extension of the Gottesman–Knill theorem that applies to universal computation, showing that for Clifford circuits with joint

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Caha, Libor, Xavier Coiteux-Roy, and Robert Koenig. "Single-qubit gate teleportation provides a quantum advantage." Quantum 8 (December 4, 2024): 1548. https://doi.org/10.22331/q-2024-12-04-1548.

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Gate-teleportation circuits are arguably among the most basic examples of computations believed to provide a quantum computational advantage: In seminal work \cite{TerhalDiVincenzo04}, Terhal and DiVincenzo have shown that these circuits elude simulation by efficient classical algorithms under plausible complexity-theoretic assumptions. Here we consider possibilistic simulation \cite{wang2021possibilistic}, a particularly weak form of this task where the goal is to output any string appearing with non-zero probability in the output distribution of the circuit. We show that even for single-qubi

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Ferrara, Roberto, Riccardo Bassoli, Christian Deppe, Frank H. P. Fitzek, and Holger Boche. "The Computational and Latency Advantage of Quantum Communication Networks." IEEE Communications Magazine 59, no. 6 (2021): 132–37. http://dx.doi.org/10.1109/mcom.011.2000863.

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SAKAI, Mikio. "Learning Advantage of Computational Granular Dynamics from the Applications." JAPANESE JOURNAL OF MULTIPHASE FLOW 32, no. 3 (2018): 314–20. http://dx.doi.org/10.3811/jjmf.2018.t005.

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Ramachandran, M. P., and M. P. Ramachandran. "Computational advantage in evaluating oscillatory integral using quadratic spline." International Journal of Computing Science and Mathematics 19, no. 1 (2024): 28–38. http://dx.doi.org/10.1504/ijcsm.2024.10062420.

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Ramachandran, M. P., and M. P. Ramachandran. "Computational advantage in evaluating oscillatory integral using quadratic spline." International Journal of Computing Science and Mathematics 19, no. 1 (2024): 28–38. http://dx.doi.org/10.1504/ijcsm.2024.136812.

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13

Xu, Xuebin, Kan Meng, Chen Chen, and Longbin Lu. "Isolated Word Sign Language Recognition Based on Improved SKResNet-TCN Network." Journal of Sensors 2023 (July 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/9503961.

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This paper proposes an improved selective kernel network-temporal convolutional (SKResNet-TCN) network-based video recognition model for isolated word sign language with too large parameters, large computation, and difficult to extract effective features. SKResNet uses grouped convolution to save computational cost while dynamically selecting feature information of different perceptual fields to improve the feature extraction ability of the model for video frame images, and TCN introduces causal and inflation convolution to take full advantage of computer parallel computing and reduce memory o

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Brod, Daniel Jost. "Loops simplify a set-up to boost quantum computational advantage." Nature 606, no. 7912 (2022): 31–32. http://dx.doi.org/10.1038/d41586-022-01402-x.

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Torres Torres, Noemi, Dra Alicia Sierra Diaz, and Eulalia Torres Torres. "Computational Geointelligence: A Strategy to Achieve Competitive Advantage in Organizations." International Journal of Managerial Studies and Research 12, no. 2 (2024): 1–14. http://dx.doi.org/10.20431/2349-0349.1202001.

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Hindlycke, Christoffer, Niklas Johansson, and Jan-Åke Larsson. "Phase Coordinate Uncomputation in Quantum Recursive Fourier Sampling." Entropy 27, no. 6 (2025): 596. https://doi.org/10.3390/e27060596.

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Recursive Fourier Sampling (RFS) was one of the earliest problems to demonstrate a quantum advantage, and is known to lie outside the Merlin–Arthur complexity class. This work contains a new description of quantum algorithms in phase space terminology, demonstrating its use in RFS, and how and why this gives a better understanding of the quantum advantage in RFS. Most importantly, describing the computational process of quantum computation in phase space terminology gives a much better understanding of why uncomputation is necessary when solving RFS: the advantage is present only when phase co

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El-Seoud, Samir Abou, Reham Fouad Mohamed, and Samy Ghoneimy. "DNA Computing: Challenges and Application." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 2 (2017): 74. http://dx.doi.org/10.3991/ijim.v11i2.6564.

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<p class="Abstract">Much of our scientific, technological, and economic future depends on the availability of an ever-increasing supply of computational power. However, the increasing demand for such power has pushed electronic technology to the limit of physical feasibility and has raised the concern that this technology may not be able to sustain our growth in the near future. It became important to consider an alternative means of achieving computational power. In this regard, DNA computing was introduced based on the usage of DNA and molecular biology hardware instead of the typical

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Abras, Jennifer N., C. Eric Lynch, and Marilyn J. Smith. "Computational Fluid Dynamics–Computational Structural Dynamics Rotor Coupling Using an Unstructured Reynolds-Averaged Navier–Stokes Methodology." Journal of the American Helicopter Society 57, no. 1 (2012): 1–14. http://dx.doi.org/10.4050/jahs.57.012001.

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The focus of this paper is to discuss the unique challenges introduced through the use of unstructured grids in rotorcraft computational fluid dynamics (CFD)–computational structural dynamics (CSD) coupling. The use of unstructured grid methodology in CFD has been expanding because of the advantages in grid generation and modeling of complex configurations. However, the resulting amorphous distribution of the grid points on the rotor blade surface provides no information with regard to the orientation of the blade, in direct contrast to structured grid methodology that can take advantage of th

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Tan, Xincai, Christopher E. Goodyer, Peter K. Jimack, Robert I. Taylor, and Mark A. Walkley. "Computational approaches for modelling elastohydrodynamic lubrication using multiphysics software." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 226, no. 6 (2012): 463–80. http://dx.doi.org/10.1177/1350650111428028.

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Elastohydrodynamic lubrication modelling plays an important role in engineering design and analysis, since a number of important mechanical components operate under elastohydrodynamic lubrication conditions. In this article, methods are presented for solving both line and point contact cases using multiphysics software. The advantages, and the overheads, of using such an approach over developing highly specialised, bespoke software are highlighted. In order to calculate the deformation of the contacts three different methods are developed and their relative performance is assessed. The advanta

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Zhu, Qingling, Sirui Cao, Fusheng Chen, et al. "Quantum computational advantage via 60-qubit 24-cycle random circuit sampling." Science Bulletin 67, no. 3 (2022): 240–45. http://dx.doi.org/10.1016/j.scib.2021.10.017.

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Cekirge, H. M., M. Koch, C. Long, et al. "STATE-OF-THE-ART TECHNIQUES IN OIL SPILL MODELING." International Oil Spill Conference Proceedings 1995, no. 1 (1995): 67–72. http://dx.doi.org/10.7901/2169-3358-1995-1-67.

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ABSTRACT The status and possible research directions of oil spill modeling are presented here. The physical and chemical processes that take place in oil spills are explained as is their role in the design of an ideal oil spill model. An ideal oil spill model for forecasting must support rapid response, contingency planning, and training. Accurate, full-dimension, real-time prediction of hydrodynamic calculations and oil movements require intensive computations and computing power. Presently the most promising computational platform appears to be a vector supercomputer that has been given esse

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Thomas, Rajesh, Victor DeBrunner, and Linda S. DeBrunner. "A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors." Signals 2, no. 4 (2021): 688–705. http://dx.doi.org/10.3390/signals2040041.

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Direct computation of the discrete Fourier transform (DFT) and its FFT computational algorithms requires multiplication (and addition) of complex numbers. Complex number multiplication requires four real-valued multiplications and two real-valued additions, or three real-valued multiplications and five real-valued additions, as well as the requisite added memory for temporary storage. In this paper, we present a method for computing a DFT via a natively real-valued algorithm that is computationally equivalent to a N=2k-length DFT (where k is a positive integer), and is substantially more effic

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Navia-Vázquez, A., and A. R. Figueiras-Vidal. "Efficient Block Training of Multilayer Perceptrons." Neural Computation 12, no. 6 (2000): 1429–47. http://dx.doi.org/10.1162/089976600300015448.

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The attractive possibility of applying layerwise block training algorithms to multilayer perceptrons MLP, which offers initial advantages in computational effort, is refined in this article by means of introducing a sensitivity correction factor in the formulation. This results in a clear performance advantage, which we verify in several applications. The reasons for this advantage are discussed and related to implicit relations with second-order techniques, natural gradient formulations through Fisher's information matrix, and sample selection. Extensions to recurrent networks and other resea

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Kenigsberg, D., A. Mor, and G. Ratsaby. "Quantum advantage without entanglement." Quantum Information and Computation 6, no. 7 (2006): 606–15. http://dx.doi.org/10.26421/qic6.7-4.

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We study the advantage of pure-state quantum computation without entanglement over classical computation. For the Deutsch-Jozsa algorithm we present the \emph{maximal} subproblem that can be solved without entanglement, and show that the algorithm still has an advantage over the classical ones. We further show that this subproblem is of greater significance, by proving that it contains all the Boolean functions whose quantum phase-oracle is non-entangling. For Simon's and Grover's algorithms we provide simple proofs that no non-trivial subproblems can be solved by these algorithms without enta

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Li, Qianqian, Shutian Zhou, Xiangrong Zeng, et al. "A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency." Applied Sciences 15, no. 6 (2025): 3275. https://doi.org/10.3390/app15063275.

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This paper proposes a data security training framework based on symmetric projection space and adversarial training, aimed at addressing the issues of privacy leakage and computational efficiency encountered by current privacy protection technologies when processing sensitive data. By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, reca

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Chabaud, Ulysse, Roohollah Ghobadi, Salman Beigi, and Saleh Rahimi-Keshari. "Phase-space negativity as a computational resource for quantum kernel methods." Quantum 8 (November 7, 2024): 1519. http://dx.doi.org/10.22331/q-2024-11-07-1519.

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Quantum kernel methods are a proposal for achieving quantum computational advantage in machine learning. They are based on a hybrid classical-quantum computation where a function called the quantum kernel is estimated by a quantum device while the rest of computation is performed classically. Quantum advantages may be achieved through this method only if the quantum kernel function cannot be estimated efficiently on a classical computer. In this paper, we provide sufficient conditions for the efficient classical estimation of quantum kernel functions for bosonic systems. These conditions are b

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Michelena, R. J., and J. M. Harris. "Tomographic traveltime inversion using natural pixels." GEOPHYSICS 56, no. 5 (1991): 635–44. http://dx.doi.org/10.1190/1.1443080.

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Traditionally in the problem of tomographic traveltime inversion, the model is divided into a number of rectangular cells of constant slowness. Inversion consists of finding these constant values using the measured traveltimes. The inversion process can demand a large computational effort if a high‐resolution result is desired. We show how to use a different kind of parameterization of the model based on beam propagation paths. This parameterization is obtained within the framework of reconstruction in Hilbert spaces by minimizing the error between the true model and the estimated model. The t

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Hubbart, Jason A. "The Computational Pivot: Turning Fiscal Austerity into Research Advantage at Public Universities." IOSR Journal of Research & Method in Education 15, no. 4 (2025): 19–29. https://doi.org/10.9790/7388-1504011929.

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Public research universities face converging pressures: shrinking appropriations, decreasing grant success rates, and rising facility costs. However, the last decade has also seen the emergence of inexpensive cloud cycles, opensource analytics, and exabyte-scale data that redefine discovery. This article presents an argument that a focused transition to computational innovation, simulation, artificial intelligence, and data-intensive methodologies offers the potential to transform resource scarcity into a strategic advantage. Shared cyberinfrastructure has the potential to reduce project expen

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Sharma, Mrs Anush. "PARALLEL COMPUTING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem37812.

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Parallel computing is a computational technique in which two or more tasks or instructions can be processed concurrently by means of several processors resulting into enhanced computational efficiency as well as decreased time for solving complex problems. As an approach to solve a particular problem, parallel computing is an attempt that decomposes a problem into constituent subproblems to be solved concurrently; it uses multiple cores, hardware systems to gain advantage in speed The use of parallel computing is prominent in sciences that call for massive computations such as simulations, dat

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MA, HAIBO, CHUNGEN LIU, and YUANSHENG JIANG. "BLOCK DENSITY MATRIX RENORMALIZATION GROUP WITH EFFECTIVE INTERACTIONS." Journal of Theoretical and Computational Chemistry 08, no. 05 (2009): 837–48. http://dx.doi.org/10.1142/s0219633609005064.

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Based on the contractor renormalization group (CORE) method and the density matrix renormalization group (DMRG) method, a new computational scheme, which is called the block density matrix renormalization group with effective interactions (BDMRG-EI), is proposed to deal with the numerical computation of quantum correlated systems. Different from the conventional CORE method in calculating the blocks and the fragments, where the DMRG method instead of the exact diagonalization is employed in BDMRG-EI, BDMRG-EI makes the calculations of larger blocks and fragments applicable. Integrating DMRG's

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Ryatina, E. P., I. K. Marchevsky, A. O. Kolganova, and D. Yu Kobzar. "Implementation of fast algorithms for 2D flow simulation using vortex particle methods." Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki 167, no. 1 (2025): 99–114. https://doi.org/10.26907/2541-7746.2025.1.99-114.

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Vortex particle methods of computational hydrodynamics are widely employed by engineers to solve the problems of flow simulation and estimation of unsteady hydrodynamic loads acting on bodies. The main advantage of such methods is a relatively low computational cost, but their applicability is limited to subsonic incompressible single-phase non-heat-conducting flows. If high order discretization is required, the usage of direct algorithms leads to a significant increase in computational complexity and memory demand. To overcome this limitation, approximate fast algorithms of quasilinear comput

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Mohammed W. Al-Neam. "A Novel Parallel Approach to Image Processing for High-Performance Computing." Journal of Information Systems Engineering and Management 10, no. 7s (2025): 721–29. https://doi.org/10.52783/jisem.v10i7s.985.

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Introduction: With the rise of big data and increasing computational demands, the need for advanced image processing techniques has become critical. High-resolution images and computationally intensive algorithms often exceed the capabilities of traditional serial computations, making them insufficient for modern requirements. Objectives: This paper introduces a new parallel algorithm designed specifically for image processing in high-performance computing environments. The primary goal is to enhance response times, reduce computation durations, and ensure scalability for large and complex ima

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Purwadi, Purwadi, Nor Azman Abu, Othman Bin Mohd, and Bagus Adhi Kusuma. "Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods." JOIV : International Journal on Informatics Visualization 7, no. 3 (2023): 910. http://dx.doi.org/10.30630/joiv.7.3.1758.

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Hyperspectral image technology in land classification is a distinct advantage compared to ordinary RGB and multispectral images. This technology has a wide spectrum of electromagnetic waves, which can be more detailed than other types of imagery. Therefore, with its hyperspectral advantages, the characteristics of an object should have a high probability of being recognized and distinguished. However, because of the large data, it becomes a challenge to lighten the computational burden. Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because

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Borude, Priyanka Hari. "Computational Toxicology." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 273–78. http://dx.doi.org/10.22214/ijraset.2024.64495.

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Abstract: The discipline of “computational” toxicology has been studied thoroughly and its applications have been implemented in the field to the advantage of many individuals by providing cheap and rapid solutions for chemical screening.The current review focuses on the recent progress, obstacles, and use of computational toxicology with respect to various predicted within toxicity endpoints; carcinogenicity, mutagenicity and reproductive spanning toxicity in males. The combination of these approaches involving quantitative structure-activity relationship (QSAR) alongside machine learning (ML

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Eastham, Sebastian D., Michael S. Long, Christoph A. Keller, et al. "GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications." Geoscientific Model Development 11, no. 7 (2018): 2941–53. http://dx.doi.org/10.5194/gmd-11-2941-2018.

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Abstract. Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ∼100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architect

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Seisa, Achilleas Santi, Sumeet Gajanan Satpute, Björn Lindqvist, and George Nikolakopoulos. "An Edge-Based Architecture for Offloading Model Predictive Control for UAVs." Robotics 11, no. 4 (2022): 80. http://dx.doi.org/10.3390/robotics11040080.

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Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using compu

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Cortés, J. C., J. V. Romero, M. D. Roselló, Francisco-J. Santonja, and Rafael-J. Villanueva. "Solving Continuous Models with Dependent Uncertainty: A Computational Approach." Abstract and Applied Analysis 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/983839.

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This paper presents a computational study on a quasi-Galerkin projection-based method to deal with a class of systems of random ordinary differential equations (r.o.d.e.’s) which is assumed to depend on a finite number of random variables (r.v.’s). This class of systems of r.o.d.e.’s appears in different areas, particularly in epidemiology modelling. In contrast with the other available Galerkin-based techniques, such as the generalized Polynomial Chaos, the proposed method expands the solution directly in terms of the random inputs rather than auxiliary r.v.’s. Theoretically, Galerkin project

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Zhang, Jie, Guillaume Michal, Ahn Kiet Tieu, Hong Tao Zhu, and Guan Yu Deng. "Hertz Contact at the Nanoscale with a 3D Multiscale Model." Applied Mechanics and Materials 846 (July 2016): 306–11. http://dx.doi.org/10.4028/www.scientific.net/amm.846.306.

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This paper presents a three-dimensional multiscale computational model, which is proposed to combine the simplicity of FEM model and the atomistic interactions between two solids. A significant advantage of the model is that atoms are populated in the contact regions, which saves significant computation time compared to fully MD simulations. The model is used in the case of asperity contact. The normal displacement, contact radius and pressure distribution are compared with those from Hertz’s solution and atomistic simulations in the literature. Some important features of nanoscale contacts ob

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Mallma Ramirez, Lennin, Nelson Maculan, Adilson Elias Xavier, and Vinicius Layter Xavier. "Dislocation hyperbolic augmented Lagrangian algorithm in convex programming." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 14, no. 2 (2024): 147–55. http://dx.doi.org/10.11121/ijocta.1402.

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The dislocation hyperbolic augmented Lagrangian algorithm (DHALA) is a new approach to the hyperbolic augmented Lagrangian algorithm (HALA). DHALA is designed to solve convex nonlinear programming problems. We guarantee that the sequence generated by DHALA converges towards a Karush-Kuhn-Tucker point. We are going to observe that DHALA has a slight computational advantage in solving the problems over HALA. Finally, we will computationally illustrate our theoretical results.

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Sidko, Alla. "What is New in the Latest Release of Mathpar-DAP Runtime." NaUKMA Research Papers. Computer Science 6 (March 24, 2024): 76–80. http://dx.doi.org/10.18523/2617-3808.2023.6.76-80.

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In this paper, we recall the main features of the DAP runtime, that was published in [4]. But the main purpose of this paper is to describe the new functionality that appeared in our latest release. As an example of a block recursive algorithm, the Cholesky factorization of a symmetric positive definite matrix in the form of a block dichotomous algorithm is described. The results of experiments demonstrate good scalability of the proposed solution. Modern supercomputer systems containing hundreds of thousands of cores face difficulties in the organization of parallel computations (e.g., see [1

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Fu, Xing, and J. Nathan Kutz. "Adaptive Dimensionality-Reduction for Time-Stepping in Differential and Partial Differential Equations." Numerical Mathematics: Theory, Methods and Applications 10, no. 4 (2017): 872–94. http://dx.doi.org/10.4208/nmtma.2017.m1624.

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AbstractA numerical time-stepping algorithm for differential or partial differential equations is proposed that adaptively modifies the dimensionality of the underlying modal basis expansion. Specifically, the method takes advantage of any underlying low-dimensional manifolds or subspaces in the system by using dimensionality-reduction techniques, such as the proper orthogonal decomposition, in order to adaptively represent the solution in the optimal basis modes. The method can provide significant computational savings for systems where low-dimensional manifolds are present since the reductio

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Rasool, Hafiz, Chen Jun, Xiao-Min Pan, and Xin-Qing Sheng. "Skeletonization Accelerated Solution of Crank-Nicolson Method for Solving Three-Dimensional Parabolic Equation." Applied Computational Electromagnetics Society 35, no. 9 (2020): 1006–11. http://dx.doi.org/10.47037/2020.aces.j.350905.

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Parabolic equation models discretized with the finite difference method have been extensively studied for a long time. However, several explicit and implicit schemes exist in the literature. The advantage in explicit schemes is its simplicity, while its disadvantage is conditional stability. On the other hand, implicit schemes are unconditionally stable but require special treatment for a fast and accurate solution such as the Crank-Nicolson (CN) method. This method becomes computationally intensive for problems with dense meshes. The resulting matrix from the CN in two and three-dimensional c

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Li, Yanru, and Jun Zhu. "16-Bit multiplier optimization based on Wallace tree and Booth algorithm." Applied and Computational Engineering 36, no. 1 (2024): 204–9. http://dx.doi.org/10.54254/2755-2721/36/20230447.

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With the expanding computer application scenarios and increasing computational demands, optimizing 16-bit multiplication is an important and meaningful research topic. In this paper, we take advantage of the parallel computing property of Wallace tree and the advantage of bit-level operation of Booth algorithm to perform certain optimization on 16-bit multiplier. In this paper, a series of basic modules are firstly constructed as the basic framework of the multiplier, and then the optimization module is designed by using Booth algorithm and Wallace tree, and they are combined to obtain a multi

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Zhou, Min-Gang, Xiao-Yu Cao, Yu-Shuo Lu, et al. "Experimental Quantum Advantage with Quantum Coupon Collector." Research 2022 (April 30, 2022): 1–11. http://dx.doi.org/10.34133/2022/9798679.

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An increasing number of communication and computational schemes with quantum advantages have recently been proposed, which implies that quantum technology has fertile application prospects. However, demonstrating these schemes experimentally continues to be a central challenge because of the difficulty in preparing high-dimensional states or highly entangled states. In this study, we introduce and analyze a quantum coupon collector protocol by employing coherent states and simple linear optical elements, which was successfully demonstrated using realistic experimental equipment. We showed that

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Piastou, Mikita. "Investigating the Impact of Quantum Computing on Algorithmic Complexity." International Journal of Scientific Research and Management (IJSRM) 12, no. 10 (2024): 1467–76. http://dx.doi.org/10.18535/ijsrm/v12i10.ec01.

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This paper investigated the transformation that quantum computing brought into algorithmic complexity in the theoretical setting of computer science. This involved the appearance of new paradigm changes brought forth by quantum technologies, imposing on a glance at the performance of quantum algorithms in respect to classical models of computation regarding their effectiveness. Our contributions encompassed key quantum algorithms, specifically Shor's and Grover's algorithms, underlining the performance metrics of those algorithms. The mathematical modeling was supported by simulations using py

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Hall, Michael J., Neil E. Olson, and Roger D. Chamberlain. "Utilizing Virtualized Hardware Logic Computations to Benefit Multi-User Performance." Electronics 10, no. 6 (2021): 665. http://dx.doi.org/10.3390/electronics10060665.

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Recent trends in computer architecture have increased the role of dedicated hardware logic as an effective approach to computation. Virtualization of logic computations (i.e., by sharing a fixed function) provides a means to effectively utilize hardware resources by context switching the logic to support multiple data streams of computation. Multiple applications or users can take advantage of this by using the virtualized computation in an accelerator as a computational service, such as in a software as a service (SaaS) model over a network. In this paper, we analyze the performance of virtua

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Chadha, Rohit, John C. Mitchell, Andre Scedrov, and Vitaly Shmatikov. "Contract signing, optimism, and advantage." Journal of Logic and Algebraic Programming 64, no. 2 (2005): 189–218. http://dx.doi.org/10.1016/j.jlap.2004.09.003.

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Luo, Xiao, Caihai Zou, Haoqiang Wu, Boyang Gao, Hongjian Sun, and Zongshuai Jin. "A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids." Processes 13, no. 1 (2025): 227. https://doi.org/10.3390/pr13010227.

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Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion o

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Escandón-Monardes, Jorge, Aldo Delgado, and Stephen P. Walborn. "Practical computational advantage from the quantum switch on a generalized family of promise problems." Quantum 7 (March 9, 2023): 945. http://dx.doi.org/10.22331/q-2023-03-09-945.

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The quantum switch is a quantum computational primitive that provides computational advantage by applying operations in a superposition of orders. In particular, it can reduce the number of gate queries required for solving promise problems where the goal is to discriminate between a set of properties of a given set of unitary gates. In this work, we use Complex Hadamard matrices to introduce more general promise problems, which reduce to the known Fourier and Hadamard promise problems as limiting cases. Our generalization loosens the restrictions on the size of the matrices, number of gates a

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Minnaert, Ben, Giuseppina Monti, and Mauro Mongiardo. "Unified Representation of 3D Multivectors with Pauli Algebra in Rectangular, Cylindrical and Spherical Coordinate Systems." Symmetry 14, no. 8 (2022): 1684. http://dx.doi.org/10.3390/sym14081684.

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In practical engineering, the use of Pauli algebra can provide a computational advantage, transforming conventional vector algebra to straightforward matrix manipulations. In this work, the Pauli matrices in cylindrical and spherical coordinates are reported for the first time and their use for representing a three-dimensional vector is discussed. This method leads to a unified representation for 3D multivectors with Pauli algebra. A significant advantage is that this approach provides a representation independent of the coordinate system, which does not exist in the conventional vector perspe

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