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Quantum Algorithms and Applications for Open Quantum Systems

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arXiv 2024年

作者： Delgado-Granados, Luis H. Krogmeier, Timothy J. Sager-Smith, LeeAnn M. Avdic, Irma Hu, Zixuan Sajjan, Manas Abbasi, Maryam Smart, Scott E. Narang, Prineha Kais, Sabre Schlimgen, Anthony W. Head-Marsden, Kade Mazziotti, David A. Department of Chemistry The James Franck Institute The University of Chicago ChicagoIL60637 United States Department of Chemistry Washington University in St. Louis St. LouisMO61630 United States Department of Chemistry University of Minnesota MinneapolisMN55455 United States Department of Chemistry and Physics Saint Mary’s College Notre DameIN46556 United States Department of Chemistry Department of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei230026 China Division of Physical Sciences College of Letters and Science University of California Los AngelesCA90095 United States

Accurate models for open quantum systems—quantum states that have non-trivial interactions with their environment—may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

Resilient to Byzantine Attacks Finite-Sum Optimization Over Networks

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Resilient to Byzantine Attacks Finite-Sum Optimization Over ...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhaoxian Wu Qing Ling Tianyi Chen Georgios B. Giannakis School of Data and Computer Science and Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the friendly workers. This motivates gradient noise reduction as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to achieve provable linear convergence to a neighborhood of the optimal solution, where the size of neighborhood is determined by the number of Byzantine workers. Numerical tests demonstrate the robustness of Byrd-SAGA to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine-resilient SGD.

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Challenges of online learning for children with special educational needs and disabilities during the COVID-19 pandemic: A scoping review

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Australian Journal of Developmental Disabilities 2023年第2期48卷 105-116页

作者： Indre Bakaniene Martyna Dominiak-Świgoń Miguel Augusto Meneses da Silva Santos Dimitris Pantazatos Mary Grammatikou Marco Montanari Irene Virgili Giovanni Galeoto Paolo Flocco Laura Bernabei Audrone Prasauskiene a Department of Children's Rehabilitation Lithuanian University of Health Sciences Kaunas Lithuania b Poznan Supercomputing and Networking Center Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland c Centre for Research and Innovation in Education School of Education Polytechnic Institute of Porto Porto Portugal d School of Electrical & Computer Engineering National Technical University of Athens Zografou Greece e ISO Department Sapienza University of Rome Rome Italy f Universita degli Studi Roma Tre Centro di Ateneo per lo Studio di Roma Casa Santa Rosa Rehabilitation Center Roma Italy g Department of Human Neurosciences Sapienza University of Rome Rome Italy h Department of Human Neuroscience Sapienza University of Rome Rome Italy i Mental Health Department Sapienza University Rome Italy

ABSTRACTABSTRACTBackgroundThe COVID-19 pandemic has affected education at all levels in various ways. This paper provides a review of the literature on the challenges of online learning for children with special educational needs and disabilities (SEND).MethodIn total, 17 studies from nine countries were analysed. The challenges of online learning for children with SEND reported by teachers and parents and the strategies applied to overcome the challenges were *** challenges of online learning were the need of parental support, routine change, inequities of resources and access to technology, lack of accommodations, and social isolation. There was a considerable lack of interventions aimed at promoting the educational outcomes of children with *** findings emphasise the importance of parent-teacher collaboration and communication. Suggestions for strategies to address challenges as well as for further research are also discussed.

关键词： Children COVID-19 pandemic disabilities online learning special educational needs

Transport Study of Charge Carrier Scattering in Monolayer WSe2

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arXiv 2023年

作者： Joe, Andrew Y. Pistunova, Kateryna Kaasbjerg, Kristen Wang, Ke Kim, Bumho Rhodes, Daniel A. Taniguchi, Takashi Watanabe, Kenji Hone, James Low, Tony Jauregui, Luis A. Kim, Philip Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark Department of Mechanical Engineering Columbia University New YorkNY10027 United States National Institute for Materials Science Tsukuba Japan Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Dept. of Physics and Astronomy The University of California IrvineCA92697 United States

Employing flux-grown single crystal WSe2, we report charge carrier scattering behaviors measured in h-BN encapsulated monolayer field effect transistors. We perform quantum transport measurements across various hole densities and temperatures and observe a non-monotonic change of transport mobility µ as a function of hole density in the degenerately doped sample. This unusual behavior can be explained by energy dependent scattering amplitude of strong defects calculated using the T-matrix approximation. Utilizing long mean-free path (>500 nm), we demonstrate the high quality of our electronic devices by showing quantized conductance steps from an electrostatically-defined quantum point contact. Our results show the potential for creating ultra-high quality quantum optoelectronic devices based on atomically thin semiconductors. © 2023, CC BY.

关键词： Monolayers

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

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arXiv 2024年

作者： Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. Stanford Center for Artificial Intelligence in Medicine and Imaging Stanford University Palo AltoCA United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Big Data Institute University of Oxford Oxford United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology University Hospital Zurich Zürich Switzerland Stability AI London United Kingdom Jülich Supercomputing Centre Jülich Germany LAION Germany Department of Biomedical Data Science Stanford University StanfordCA United States Department of Medicine Stanford University StanfordCA United States

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to streamline CXR interpretation and documentation. While foundation models are a promising solution, the lack of publicly available large-scale datasets and benchmarks inhibits their iterative development and real-world evaluation. To overcome these challenges, we constructed a large-scale dataset (CheXinstruct), which we utilized to train a vision-language foundation model (CheXagent). We systematically demonstrated competitive performance across eight distinct task types on our novel evaluation benchmark (CheXbench). Beyond technical validation, we assessed the real-world utility of CheXagent in directly drafting radiology reports. Our clinical assessment with eight radiologists revealed a 36% time saving for residents using CheXagent-drafted reports, while attending radiologists showed no significant time difference editing resident-drafted or CheXagent-drafted reports. The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality. Overall, we demonstrate that CheXagent can effectively perform a variety of CXR interpretation tasks and holds potential to assist radiologists in routine clinical workflows. © 2024, CC BY.

关键词： Visual languages

SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology supercomputing Synchrotron X-rays

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

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arXiv 2022年

作者： Hammernik, Kerstin Küstner, Thomas Yaman, Burhaneddin Huang, Zhengnan Rueckert, Daniel Knoll, Florian Akçakaya, Mehmet The Institute of AI and Informatics in Medicine Technical University of Munich The Department of Computing Imperial College London United Kingdom The Department of Diagnostic and Interventional Radiology University Hospital of Tuebingen Germany The Department of Electrical and Computer Engineering Center for Magnetic Resonance Research University of Minnesota United States The Center for Biomedical Imaging Department of Radiology New York University United States The Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen Germany

Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. We highlight domain-specific challenges such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and non-linear forward models. Finally, we discuss common issues and open challenges, and draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Convert Widespread Paraelectric Perovskite to Ferroelectrics

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Physical Review Letters 2022年第19期128卷 197601-197601页

作者： Hongwei Wang Fujie Tang Massimiliano Stengel Hongjun Xiang Qi An Tony Low Xifan Wu Department of Physics Temple University Philadelphia Pennsylvania 19122 USA Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Institut de Ciència de Materials de Barcelona (ICMAB-CSIC) Campus UAB 08193 Bellaterra Spain ICREA-Institució Catalana de Recerca i Estudis Avançats 08010 Barcelona Spain Key Laboratory of Computational Physical Sciences (Ministry of Education) State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200438 People’s Republic of China Department of Chemical and Materials Engineering University of Nevada-Reno Reno Nevada 89557 USA Institute for Computational Molecular Science Temple University Philadelphia Pennsylvania 19122 USA

While nature provides a plethora of perovskite materials, only a few exhibit large ferroelectricity and possibly multiferroicity. The majority of perovskite materials have the nonpolar CaTiO3(CTO) structure, limiting the scope of their applications. Based on the effective Hamiltonian model as well as first-principles calculations, we propose a general thin-film design method to stabilize the functional BiFeO3(BFO)-type structure, which is a common metastable structure in widespread CTO-type perovskite oxides. It is found that the improper antiferroelectricity in CTO-type perovskite and ferroelectricity in BFO-type perovskite have distinct dependences on mechanical and electric boundary conditions, both of which involve oxygen octahedral rotation and tilt. The above difference can be used to stabilize the highly polar BFO-type structure in many CTO-type perovskite materials.

关键词： Ferroelectricity Dielectrics Multiferroics Perovskites First-principles calculations

Retrieval-augmented in-context learning for multimodal large language models in disease classification

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arXiv 2025年

作者： Zhan, Zaifu Zhou, Shuang Zhou, Xiaoshan Xiao, Yongkang Wang, Jun Deng, Jiawen Zhu, He Hou, Yu Zhang, Rui Department of Electrical and Computer Engineering University of Minnesota 200 Union St SE MinneapolisMN55455 United States Division of Computational Health Sciences Department of Surgery University of Minnesota 516 Delaware St SE MinneapolisMN55455 United States Department of Civil and Environmental Engineering University of Michigan 2350 Hayward Street Ann ArborMI48109 United States Institute for Health Informatics University of Minnesota 516 Delaware Street SE MinneapolisMN55455 United States Department of Computer Science and Engineering University of Minnesota 200 Union St SE MinneapolisMN55455 United States Department of Chemical Engineering and Materials Science University of Minnesota 421 Washington Ave. SE MinneapolisMN55455 United States

Objectives: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. Methods: We propose a Retrieval-Augmented In-Context Learning (RAICL) framework, which integrates retrieval-augmented generation (RAG) and in-context learning (ICL) to adaptively select demonstrations with similar disease patterns, enabling more effective ICL in MLLMs. Specifically, RAICL examines embeddings from diverse encoders, including ResNet, BERT, BioBERT, and ClinicalBERT, to retrieve appropriate demonstrations, and constructs conversational prompts optimized for ICL. We evaluated the framework on two real-world multi-modal datasets (TCGA and IU Chest X-ray), assessing its performance across multiple MLLMs (Qwen, Llava, Gemma), embedding strategies, similarity metrics, and varying numbers of demonstrations. Results: RAICL consistently improved classification performance. Accuracy increased from 0.7854 to 0.8368 on TCGA and from 0.7924 to 0.8658 on IU Chest X-ray. Multi-modal inputs outperformed single-modal ones, with text-only inputs being stronger than images alone. The richness of information embedded in each modality will determine which embedding model can be used to get better results. Few-shot experiments showed that increasing the number of retrieved examples further enhanced performance. Across different similarity metrics, Euclidean distance achieved the highest accuracy while cosine similarity yielded better macro-F1 scores. RAICL demonstrated consistent improvements across various MLLMs, confirming its robustness and versatility. Conclusions: RAICL provides an efficient and scalable approach to enhance in-context learning in MLLMs for multimodal disease classification. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings