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Toward Accurate Post-Born-Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals

arXiv

引用

arXiv 2023年

作者： Nykänen, Anton Miller, Aaron Talarico, Walter Knecht, Stefan Kovyrshin, Arseny Skogh, Mårten Tornberg, Lars Broo, Anders Mensa, Stefano Symons, Benjamin C.B. Sahin, Emre Crain, Jason Tavernelli, Ivano Pavošević, Fabijan Algorithmiq Ltd. Kanavakatu 3C HelsinkiFI-00160 Finland School of Physics Trinity College Dublin College Green Dublin 2 Ireland Department of Applied Physics QTF Centre of Excellence Center for Quantum Engineering Aalto University School of Science AaltoFIN-00076 Finland ETH Zürich Department of Chemistry and Applied Life Sciences Vladimir-Prelog-Weg 1-5/10 Zürich8093 Switzerland Data Science and Modelling Pharmaceutical Sciences R&D AstraZeneca Gothenburg Pepparedsleden 1 MolndalSE-431 83 Sweden Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden The Hartree Centre STFC Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom IBM Research Europe Hartree Centre STFC Laboratory Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom Department of Biochemistry University of Oxford OxfordOX1 3QU United Kingdom IBM Quantum IBM Research - Zürich Rüschlikon8803 Switzerland

Nuclear quantum effects such as zero-point energy and hydrogen tunnelling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size. By contrast, quantum computers offer a means of solving this problem with polynomial scaling. However, due to the limitations of current quantum devices, NEO simulations are confined to the smallest systems described by minimal basis sets whereas realistic simulations beyond the Born-Oppenheimer approximation require more sophisticated basis sets. For this purpose, we herein extend a hardware-efficient AdAPT-VQE method to the NEO framework in the frozen natural orbital (FNO) basis. We demonstrate on H2 and d2 molecules that the NEO-FNO-AdAPT-VQE method reduces the CNOT count by a several orders of magnitude relative to the NEO unitary coupled cluster method with singles and doubles (NEO-UCCSd) while maintaining the desired accuracy. This extreme reduction in the CNOT gate count is sufficient to permit practical computations employing the NEO method - an important step toward accurate simulations involving non-classical nuclei and non-Born-Oppenheimer effects on near-term quantum devices. We further show that the method can capture isotope effects and we demonstrate that inclusion of correlation energy systematically improves the prediction of ∆ZPE. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Semi-supervised text style transfer: Cross projection in latent space

arXiv

引用

arXiv 2019年

作者： Shang, Mingyue Li, Piji Fu, Zhenxin Bing, Lidong Zhao, dongyan Shi, Shuming Yan, rui Wangxuan Institute of Computer Technology Peking University China Tencent AI Lab Shenzhen China Center for Data Science China R&D Center Singapore Machine Intelligence Technology Alibaba DAMO Academy

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese. Copyright © 2019, The Authors. All rights reserved.

关键词： Supervised learning

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Traffic Optimization Algorithms in Optical Networks For real Time Traffic Analysis

引用

Optik 2022年

作者： Suresh Kumar. K Swati Vijay Shinde S. Venkata Lakshmi r.S. Sabeenian Koppisetti durga Bhavani K.V.S.r. Murthy Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of ECE Sona College of Technology Salem Department of Management Studies Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India EEE Department & Dean (R&D) Aditya Group of Engineering Colleges

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SOrT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.

关键词： Networking Traffic Management and Analytics (NTMA) real-time surveillance traffic management and database processing

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review for the Solar radiation Forecasting Methods Based on Machine Learning Approaches

引用

Journal of Physics: Conference Series 2021年第4期1964卷

作者： U Hemavathi Ann C V Medona V dhilip Kumar r raja Sekar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Assistant Professor Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India

Predictions of solar potential for these systems' production are important, whether they ensure sound activity or the perfect control of an energy discharge heading to the solar system. It is important to base the prediction on solar irradiance before predicting solar systems performance. The measurement of solar radiation elements is a very significant criterion for applications of solar energy. Several globalized solar radiation prediction modes can be done in the two major categories: cloud imagery with physical models and machine learning techniques are correlated. In this paper, the methods used to predict solar radiation are explained with machine learning algorithms.

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Machine learning for modelling unstructured grid data in computational physics: A review

引用

Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc ding, Weiping Finn, Tobias Sebastian Fu, rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： reinforcement learning

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HHMM at semEval-2019 task 2: unsupervised frame induction using contextualized word embeddings

arXiv

引用

arXiv 2019年

作者： Anwar, Saba Ustalov, dmitry Arefyev, Nikolay Ponzetto, Simone Paolo Biemann, Chris Panchenko, Alexander Language Technology Group Department of Informatics University of Hamburg Germany Skolkovo Institute of Science and Technology Russia Data and Web Science Group University of Mannheim Germany Samsung R&D Institute Russia Lomonosov Moscow State University Russia

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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A Sparse Model-inspired deep Thresholding Network for Exponential Signal reconstruction—Application in Fast Biological Spectroscopy

arXiv

引用

arXiv 2020年

作者： Wang, Zi Guo, di Tu, Zhangren Huang, Yihui Zhou, Yirong Wang, Jian Feng, Liubin Lin, donghai You, Yongfu Agback, Tatiana Orekhov, Vladislav Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen China China Mobile Group Xiamen China Biomedical Intelligent Cloud R&D Center Xiamen University Xiamen China Department of Molecular Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg Sweden

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and many applications. deep learning has shown astonishing potential in this field but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining merits of the sparse model-based optimization method and data-driven deep learning, we propose a deep learning architecture for spectra reconstruction from undersampled data, called Modern. It follows the iterative reconstruction in solving a sparse model to build the neural network and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that Modern enables more robust, high-fidelity, and ultra-fast reconstruction than the state-of-the-art methods. remarkably, Modern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-Modern), contributing a promising strategy for further development of biological applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal reconstruction

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defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

引用

Coordination Chemistry reviews 2025年 541卷

作者： Amit Kumar Julaiba Tahsina Mazumder Kenza Joyen Frédéric Favier Ali Mirzaei Jin-Young Kim Monika Kwoka Mikhael Bechelany ravindra Kumar Jha Mahesh Kumar Hyoun Woo Kim Sang Sub Kim Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Mangaldai Assam 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS 34090 Montpellier France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS 34090 Montpellier France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Islamic Republic of Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 44-100 Gliwice Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO 2 , ZnO, Co 3 O 4 , ZrO 2 , WO 3 , and CeO 2 . It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities.

关键词： Chemiresistive gas sensing defect Engineering Metal-oxide semiconductor

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retracted: Artificial intelligence point-to-point signal communication network optimization based on ubiquitous clouds

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International Journal of Communication Systems 2020年第6期34卷

作者： Lin Hong Lianbing deng daming Li Harry Haoxiang Wang School of Management Beijing Normal University at Zhuhai Zhuhai China The Post-Doctoral Research Center Zhuhai Da Hengqin Science and Technology Development Co. Ltd Zhuhai China Department of R&D Guangdong Qinzhi Science and Technology Research Institute Zhuhai China Department of R&D Huazhong University of Science and Technology Wuhan China Institute of Data Science City University of Macau Macau China Department of R&D GoPerception Laboratory Ithaca New York USA

At present, the application of communication network has spread to every area of social life. The progress of network technology has driven the development of information technology industry and the advancement of cloud computing. With the application of artificial intelligence and deep learning technology, the network becomes more and more intelligent, so the application of artificial intelligence theory in communication network optimization modeling is more extensive. In this paper, we propose the ubiquitous clouds framework and apply the artificial intelligence optimization scheme to the point-to-point (P2P) signal transmission network optimization scheme. For different application scenarios, this paper analyzes and experiments the proposed method. The experimental results show that the proposed method makes full use of the advantages of communication network resources which improves the efficiency of communication network optimization and also reduces the optimization cost to a certain extent compared with the state-of-the-art approaches.

关键词： artificial intelligence communication network neural network optimization particle swarm optimization (PSO) point-to-point transmission ubiquitous clouds

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Accelerated MrI reconstruction with separable and enhanced low-rank Hankel regularization

arXiv

引用

arXiv 2021年

作者： Zhang, Xinlin Lu, Hengfa Guo, di Lai, Zongying Ye, Huihui Peng, Xi Zhao, Bo Qu, Xiaobo Biomedical Intelligent Cloud R&D Center Department of Electronic Science National Institute for Data Science in Health and Medicine Xiamen University Xiamen361105 China The Department of Biomedical Engineering University of Texas at Austin AustinTX78712 United States The School of Computer and Information Engineering Xiamen University of Technology Xiamen361021 China The School of Information Engineering Jimei University Xiamen361024 China The State of Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310058 China The Department of Radiology Mayo Clinic RochesterMN55902 United States The Department of Biomedical Engineering Oden Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1d vectors instead of 2d matrices from 1d vectors and thus avoid the construction of huge block Hankel matrix for 2d k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8× faster than STdLr-SPIriT, and faithful removal of undersampling artifacts. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

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