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Estimating distinguishability measures on quantum computers

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arXiv 2021年

作者： Rethinasamy, Soorya Agarwal, Rochisha Sharma, Kunal Wilde, Mark M. Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States School of Applied and Engineering Physics Cornell University IthacaNY14850 United States Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States IBM Quantum IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States

The performance of a quantum information processing protocol is ultimately judged by distinguishability measures that quantify how distinguishable the actual result of the protocol is from the ideal case. The most prominent distinguishability measures are those based on the fidelity and trace distance, due to their physical interpretations. In this paper, we propose and review several algorithms for estimating distinguishability measures based on trace distance and fidelity. The algorithms can be used for distinguishing quantum states, channels, and strategies (the last also known in the literature as "quantum combs"). The fidelity-based algorithms offer novel physical interpretations of these distinguishability measures in terms of the maximum probability with which a single prover (or competing provers) can convince a verifier to accept the outcome of an associated computation. We simulate many of these algorithms by using a variational approach with parameterized quantum circuits. We find that the simulations converge well in both the noiseless and noisy scenarios, for all examples considered. Furthermore, the noisy simulations exhibit a parameter noise resilience. Finally, we establish a strong relationship between various quantum computational complexity classes and distance estimation problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

Three-dimensional radiative flow of tetra-hybrid nanofluid via oil rig solar panel sheet with catalysis reaction using Yamad-Ota and Hamilton-Crosser models

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Journal of Radiation Research and Applied sciences 2025年第1期18卷

作者： Munawar Abbas Abdullah A. Faqihi Ilyas Khan Faisal Salah Mastoor M. Abushaega Abdulhadi A. Altherwi Muhammad Shafique Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan Department of Industrial Engineering College of Engineering and Computer Science Jazan University P. O. Box 706 Jazan 45142 Kingdom of Saudi Arabia Department of Mathematics Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science and Arts King Abdulaziz University Rabigh 21911 Saudi Arabia Industrial Engineering Department College of Engineering and Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Industrial Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Chemistry Government Graduate College Taunsa Sharif 32100 Pakistan

In this study, a framework for examining how solar radiation affects the MHD tetra hybrid nanofluid flow in an extending solar module sheet situated on an offshore solar oil field is presented. A tetra hybrid nanofluid consisting of propylene glycol ( C 3 H 8 O 2 ) as the improper fluid and A g , T i O 2 , C u and F e 3 O 4 nanoparticles are used. Renewable energy has the amazing capacity to replace itself more quickly than it is consumed, especially when it comes from natural sources like the sun and wind. This article compares the performance of the Yamada–Ota thermal conductivity tetra hybrid nanofluid model to the Hamilton–Crosser model. In addition to temperature-dependent components like thermal radiation and exponentially and temperature-dependent heat generation, the concentration aspect of tetra hybrid nanofluid is analyzed by considering both heterogeneous and homogeneous chemical processes. The temperature rise of the tetra hybrid nanofluid, which simulates the migration of nanoparticles caused by homogeneous chemical reactions inside the liquid, is investigated. Similarity factors are then used to convert the resulting partial differential equations into ordinary differential equations. After that, numerical results are acquired using the bvp4c scheme, an ODE solver. The higher heat absorbed by the PV sheet due to the increased volume percentage of tetra-hybrid nanoparticles and heat-generating features allows for a variety of applications on the offshore solar rig, such as drilling and navigation.

关键词： Cubic autocatalysis Tetra hybrid nanofluid Inclined magnetic Yamada–Ota and Hamilton–Crosser models Renewable energy Heat generation

Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2024年第3期21卷 1618-1618页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad A.Alia Raed Abu Zitar College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq 25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio′n Basada en la Informacio′n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Erratum to:J Bionic Eng https://***/10.1007/*** this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.

关键词： statement section correctly

Fuzzy c-means clustering for persistence diagrams

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arXiv 2020年

作者： Davies, Thomas Aspinall, Jack Wilder, Bryan Tran-Thanh, Long Electronics and Computer Science University of Southampton United Kingdom Department of Materials University of Oxford United Kingdom Center for Research on Computation and Society John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Computer Science University of Warwick United Kingdom

Persistence diagrams concisely represent the topology of a point cloud whilst having strong theoretical guarantees, but the question of how to best integrate this information into machine learning workflows remains open. In this paper we extend the ubiquitous Fuzzy c-Means (FCM) clustering algorithm to the space of persistence diagrams, enabling unsupervised learning that automatically captures the topological structure of data without the topological prior knowledge or additional processing of persistence diagrams that many other techniques require. We give theoretical convergence guarantees that correspond to the Euclidean case, and empirically demonstrate the capability of our algorithm to capture topological information via the fuzzy RAND index. We end with experiments on two datasets that utilise both the topological and fuzzy nature of our algorithm: pre-trained model selection in machine learning and lattices structures from materials science. As pre-trained models can perform well on multiple tasks, selecting the best model is a naturally fuzzy problem;we show that fuzzy clustering persistence diagrams allows for model selection using the topology of decision boundaries. In materials science, we classify transformed lattice structure datasets for the first time, whilst the probabilistic membership values let us rank candidate lattices in a scenario where further investigation requires expensive laboratory time and expertise. © 2020, CC BY.

关键词： Graphic methods

Wide-angle giant photonic spin Hall effect

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arXiv 2022年

作者： Chen, Zhihao Chen, Yu Wu, Yaodong Zhou, Xinxing Sun, Handong Low, Tony Chen, Hongsheng Lin, Xiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Synergetic Innovation Center for Quantum Effects and Applications School of Physics and Electronics Hunan Normal University Changsha410081 China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Institute of Microscale Optoelectronics Shenzhen University Shenzhen518060 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China

Photonic spin Hall effect is a manifestation of spin-orbit interaction of light and can be measured by a transverse shift δ of photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e. δ/λ -1, λ is the wavelength), which impedes its precise measurement. To-date proposals to generate giant spin Hall effect (namely with δ/λ > 102) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of ∆0 70◦ by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Polarization

Multiobjective optimization of a hybrid electricity generation system based on waste energy of internal combustion engine and solar system for sustainable environment

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Chemosphere 2023年第1期336卷 139269页

作者： Al-Hawary, Sulieman I.S. Ricardo Nuñez Alvarez, José Ali, Amjad Kumar Tripathi, Abhishek Rahardja, Untung Al-Kharsan, Ibrahim H. Romero-Parra, Rosario Mireya Abdulameer Marhoon, Haydar John, Vivek Hussian, Woord Department of Business Administration Business School Al al-Bayt University P.O.BOX 130040 Mafraq25113 Jordan Energy Department Universidad de la Costa Barranquilla Colombia King Fahad University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Mining Engineering Aditya Engineering College Surampalem Andhra Pradesh 533437 India Faculty of Science and Technology University of Raharja Jl. Jenderal Sudirman No.40 RT.002/RW.006 Cikokol Kec. Tangerang Banten Kota Tangerang15117 Indonesia Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf Iraq Department: General Studies Universidad Continental Lima Peru Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Medical Technical College Al-Farahidi University Baghdad Iraq

In recent years, the interest in generating power through hybrid power generation systems has increased. In this study, a hybrid power generation system including an internal combustion engine (ICE) and a solar system based on flat plate collectors to generate electricity is investigated. To benefit from the thermal energy absorbed by solar collectors, an organic Rankine cycle (ORC) is considered. In addition to the solar energy absorbed by the collectors, the heat source of the ORC is the wasted heat through exhaust gases and the cooling system of the ICE. A two-pressure configuration for ORC is proposed for optimal heat absorption from the three available heat sources. The proposed system is installed to produce power with a capacity of 10 kW. A bi-objective function optimization process is carried out to design this system. The objective of the optimization process is to minimize the total cost rate and maximize the exergy efficiency of the system. The design variables of the present problem include the ICE rated power, the number of solar flat plate collectors (SFPC), the pressure of the high-pressure (HP) and low-pressure (LP) stage of the ORC, the degree of superheating of the HP and LP stage of the ORC, and its condenser pressure. Finally, it is observed among the design variables the most impact on total cost and exergy efficiency is related to the ICE rated power and the number of SFPCs. © 2023 Elsevier Ltd

关键词： Multiobjective optimization

Exact solution for the quantum and private capacities of bosonic dephasing channels

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arXiv 2022年

作者： Lami, Ludovico Wilde, Mark M. Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 UlmD-89069 Germany QuSoft Science Park 123 Amsterdam1098 XG Netherlands Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

The capacities of noisy quantum channels capture the ultimate rates of information transmission across quantum communication lines, and the quantum capacity plays a key role in determining the overhead of fault-tolerant quantum computation platforms. In the case of bosonic systems, central to many applications, no closed formulas for these capacities were known for bosonic dephasing channels, a key class of non-Gaussian channels modelling, e.g., noise affecting superconducting circuits or fiber-optic communication channels. Here we provide the first exact calculation of the quantum, private, two-way assisted quantum, and secret-key agreement capacities of all bosonic dephasing channels. We prove that that they are equal to the relative entropy of the distribution underlying the channel to the uniform distribution. Our result solves a problem that has been open for over a decade, having been posed originally by [Jiang & Chen, Quantum and Nonlinear Optics 244, 2010]. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum communication

A practical guide to machine learning interatomic potentials – Status and future

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

Foundational Competencies and Responsibilities of a Research Software Engineer

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： computer aided software engineering