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arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Hyperspectral Image Classification via Neighborhood Adaptive Graph Isomorphism Network

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Jian Tu, Bing Liu, Bo Li, Jun Plaza, Antonio Nanjing University of Information Science and Technology Institute of Optics and Electronics State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing 210044 China China University of Geosciences (Wuhan) Faculty of Computer Science Wuhan 430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica Cáceres 10003 Spain

Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with convolutional neural network (CNN), GCN can perform convolutions over irregular image regions and learn global dependencies among pixels in the whole image. Most existing GCN-based methods in the HSI community rely on average aggregation or weighted average aggregation strategies to aggregate neighboring node features. This process tends to obscure the differences between the nodes. However, for HSI classification tasks with obvious intra-class variability, average aggregation is a suboptimal choice. Moreover, the quality of the initial graph structure plays a crucial role in the model’s capacity to represent spectral relationships effectively. To mitigate these issues, we propose a neighborhood adaptive graph isomorphism network (NAGIN) for HSI classification to ensure that the diversified spectra representation of land-cover can be effectively captured. The neighborhood adaptive block (NAB) enhances spectral discriminability between land-cover classes via spectral reconstruction, enabling more precise removal of anomalous pixels in neighboring nodes. The graph isomorphism network (GIN) aggregates the features of neighboring nodes in an isomorphic manner to obtain multiple spectral expressions of the same type of land-cover, ensuring that the spectral features of different land-cover classes can be accurately distinguished. The Kolmogorov-Arnold network (KAN) leverages its ability to learn adaptive activation functions to better extract and refine the spectral features aggregated by GIN. Experimental results demonstrate that NAB can effectively improve the quality of the graph structure, the GIN aggregation method is competitive in HSI classification, and the proposed NAGIN outperforms the state-of-the-art methods on several public HSI datasets. © 1980-2012 IEEE.

关键词： graph convolutional network (GCN) Graph isomorphism network (GIN) hyperspectral image (HSI) classification Kolmogorov-Arnold network (KAN)

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DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

arXiv

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Magnon-mediated perpendicular magnetization switching by topological crystalline insulator SnTe with high spin Hall conductivity

arXiv

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arXiv 2025年

作者： Zhao, Pengnan Shi, Guoyi Lu, Wentian Yang, Lihuan Tan, Hui Ru Guo, Kaiwei Lai, Jia-Min Yu, Zhonghai Soumyanarayanan, Anjan Yuan, Zhe Wang, Fei Xu, Xiaohong Yang, Hyunsoo Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan030006 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Singapore138634 Singapore Department of Physics National University of Singapore Singapore117551 Singapore Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan University Shanghai200433 China

Magnons possess the ability to transport spin angular momentum in insulating magnetic materials, a characteristic that sets them apart from traditional electronics where power consumption arises from the movement of electrons. However, the practical application of magnon devices demands room temperature operation and low switching power of perpendicular magnetization. Here we demonstrate the low-power manipulation of perpendicular magnetization via magnon torques in SnTe/NiO/CoFeB devices at room temperature. Topological crystalline insulator SnTe exhibits a high spin Hall conductivity of σs ≈ 6.1 x 104 (ℏ /2e) (Ω \. m)-1, which facilitates the generation of magnon currents in an antiferromagnetic insulator NiO. The magnon currents traverse the 20-nm-thick NiO layer and subsequently exert magnon torques on the adjacent ferromagnetic layer, leading to magnetization switching. Notably, we achieve a 22-fold reduction in power consumption in SnTe/NiO/CoFeB heterostructures compared to Bi2Te3/NiO/CoFeB control samples. Our findings establish the low-power perpendicular magnetization manipulation through magnon torques, significantly expanding the range of topological materials with practical applications. © 2025, CC BY-NC-ND.

关键词： Tin alloys

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Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liang, Feng Zhang, Zhen Lu, Haifeng Leung, Victor C.M. Guo, Yanyi Hu, Xiping Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518000 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518107 China Gansu Provincial Key Laboratory of Wearable Computing School of information Science and Engineering Lanzhou University Gansu730000 China The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Frontier Cross Disciplinary Research Institute Shenzhen MSU-BIT University Shenzhen518000 China The School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing10081 China

With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of algorithms and infrastructures, and heterogeneity in data sets, models, and resources. Due to intensive synchronization of models and sharing of data across GPUs and computing nodes during distributed training and inference processes, communication efficiency becomes the bottleneck for achieving high performance at a large scale. This article surveys the literature over the period of 2018-2023 on algorithms and technologies aimed at achieving efficient communication in large-scale distributed DL at various levels, including algorithms, frameworks, and infrastructures. Specifically, we first introduce efficient algorithms for model synchronization and communication data compression in the context of large-scale distributed training. Next, we introduce efficient strategies related to resource allocation and task scheduling for use in distributed training and inference. After that, we present the latest technologies pertaining to modern communication infrastructures used in distributed deep learning, including GPU interconnects, programmable network devices, collective communication protocols, and communication topologies. We focus our discussion of these topics on examining the impact of the communication overhead in a large-scale and heterogeneous setting. Finally, we conduct a case study on the distributed training of large language models at a large scale to illustrate how to apply these technologies in real cases. This article aims to offer researchers a comprehensive understanding of the current landscape of large-scale distributed deep learning and to reveal promising future research directions toward communication-efficient solutions in this scope.

关键词： Scalability

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Intensity correlations in decoy-state BB84 quantum key distribution systems

arXiv

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arXiv 2024年

作者： Trefilov, Daniil Sixto, Xoel Zapatero, Víctor Huang, Anqi Curty, Marcos Makarov, Vadim Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain atlanTTic Research Center University of Vigo VigoE-36310 Spain Russian Quantum Center Skolkovo Moscow121205 Russia National Research University Higher School of Economics Moscow101000 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The decoy-state method is a prominent approach to enhance the performance of quantum key distribution (QKD) systems that operate with weak coherent laser sources. Due to the limited transmissivity of single photons in optical fiber, current experimental decoy-state QKD setups increase their secret key rate by raising the repetition rate of the transmitter. However, this usually leads to correlations between subsequent optical pulses. This phenomenon leaks information about the encoding settings, including the intensities of the generated signals, which invalidates a basic premise of decoy-state QKD. Here we characterize intensity correlations between the emitted optical pulses in two industrial prototypes of decoy-state BB84 QKD systems and show that they significantly reduce the asymptotic key rate. In contrast to what has been conjectured, we experimentally confirm that the impact of higher-order correlations on the intensity of the generated signals can be much higher than that of nearest-neighbour correlations. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

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JUMPING FLUID MODELS AND DELAY STABILITY OF MAX-WEIGHT DYNAMICS UNDER HEAVY-TAILED TRAFFIC

arXiv

引用

arXiv 2021年

作者： Sharifnassab, Arsalan Tsitsiklis, John N. Department of Computing Science University of Alberta Edmonton Canada Laboratory for Information and Decision Systems Electrical Engineering and Computer Science Department MIT CambridgeMA02139 United States

We say that a random variable is light-tailed if moments of order 2 + ∈ are finite for some ∈ > 0;otherwise, we say that it is heavy-tailed. We study queueing networks that operate under the Max-Weight scheduling policy, for the case where some queues receive heavy-tailed and some receive light-tailed traffic. Queues with light-tailed arrivals are often delay stable (that is, expected queue sizes are uniformly bounded over time) but can also become delay unstable because of resource-sharing with other queues that receive heavy-tailed arrivals. Within this context, and for any given "tail exponents" of the input traffic, we develop a necessary and sufficient condition under which a queue is robustly delay stable, in terms of jumping fluid models—an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows. Our result elucidates the precise mechanism that leads to delay instability, through a coordination of multiple abnormally large arrivals at possibly different times and queues and settles an earlier open question on the sufficiency of a particular fluid-based criterion. Finally, we explore the power of Lyapunov functions in the study of delay stability. Copyright © 2021, The Authors. All rights reserved.

关键词： Lyapunov functions

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Optimization of performance of the KM2A full array using the Crab Nebula

引用

Chinese Physics C 2024年第6期48卷 169-178页

作者：曹臻 F.Aharonian 安琪阿西克古白云翔包逸炜 D.Bastieri 毕效军毕玉江蔡金庭曹晴曹文羽曹喆常进常劲帆陈尚明陈恩生陈亮陈林陈龙陈明君陈玛丽陈起辉陈素弘陈天禄陈阳程宁程耀东崔明阳崔树旺崔晓红崔昱东戴本忠代洪亮戴子高单增罗布 D.della Volpe 董绪强段凯凯樊军辉范一中方军方堃冯存峰封莉冯少辉丰晓婷冯有亮 S.Gabici 高博高川东高林青高启高卫高伟康葛茂茂耿利斯 G.Giacinti 龚光华苟全补顾旻皓郭福来郭晓磊郭义庆郭莹莹韩毅昂何会海贺昊宁何佳银何新波何钰 M.Heller 贺远强侯博文侯超侯贤胡红波胡铨胡世聪黄代绘黄天奇黄文俊黄性涛黄晓渊黄勇黄志成季筱璐贾焕玉贾康江琨姜晓巍姜泽军金敏康明铭柯通 D.Kuleshov.K.Kurinov 李兵兵李澄李骢李丹李飞李海波李会财李华阳李军李剑李捷李凯李文龙李文莲李秀荣李昕李一卓李哲黎卓梁恩维梁云峰林苏杰刘冰刘成刘栋刘虎刘海东刘佳刘江来刘金艳刘茂元柳若愚刘四明刘伟刘怡刘以农鲁睿罗晴吕洪魁马伯强马玲玲马欣华毛基荣闵振 W.Mitthumsiri 穆慧君南云程 A.Neronov 区子维庞彬宇 P.Pattarakijwanich 裴致远齐孟尧祁业情乔冰强秦家军 D.Ruffolo A.Sáiz D.Semikoz 邵澄宇邵琅 O.Shchegolev 盛祥东舒富文宋慧超 Yu.V.Stenkin V.Stepanov 苏扬孙秦宁孙晓娜孙志斌谭柏轩唐庆文唐泽波田文武王超王昌贝王广威王洪光王惠惠王建成汪凯王利苹王玲玉王培汉王冉王为王祥高王祥玉王阳王玉东王岩谨王忠海王仲翔王振王铮韦大明魏俊杰魏永健文韬吴超勇吴含荣武莎吴雪峰吴雨生席邵强夏捷夏君集项光漫肖迪泫肖刚辛广广辛玉良邢祎熊峥徐东莲徐仁峰徐仁新徐伟立薛良闫大海颜景志颜田杨朝文杨帆杨冯帆杨何文杨佳盈杨莉莉杨明洁杨睿智杨深邦姚玉华姚志国叶一锰尹丽巧尹娜游晓浩游智勇于艳红袁强岳华曾厚敦曾婷轩曾玮查敏张彬彬张丰张海明张恒英张建立张丽霞张力张鹏飞张佩佩张瑞张少博张少如张寿山张潇张笑鹏张云峰张毅张勇赵兵赵静赵雷赵立志赵世平郑福周斌周浩周佳能周猛周平周荣周勋秀祝成光祝凤荣朱辉朱科军左雄 LHAASO Collaboration Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Tianfu Cosmic Ray Research Center Chengdu 610000China Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 10398069029 HeidelbergGermany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Hefei 230026China School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Hebei Normal University Shijiazhuang 050024China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275China School of Physics and Astronomy Yunnan UniversityKunming 650091China Département de Physique Nucléaire et Corpusculaire Facultéde SciencesUniversitéde Genève24 Quai Ernest Ansermet1211 GenevaSwitzerland Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC Universit'e Paris Cit'eCNRS/IN2P3CEA/IRFUObservat

The full array of the Large high Altitude Air Shower Observatory(LHAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-rays using data collected from August 2021 to August 2022,resulting in an improvement in significance of the detection in the Crab Nebula of approximately 15%,compared with that of previous *** the implementation of these new selection criteria,the angular resolution was also significantly improved by approximately 10%at tens of *** aspects of the full KM2A array performance,such as the pointing error,were also calibrated using the Crab *** resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV are well fitted by a log-parabola model,which is consistent with the previous results from LHAASO and other experiments.

关键词： γ-ray Crab Nebula significance

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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Quantum advantage for differential equation analysis

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Physical Review A 2022年第2期105卷 022415-022415页

作者： Bobak Toussi Kiani Giacomo De Palma Dirk Englund William Kaminsky Milad Marvian Seth Lloyd Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Giacomo De Palma Scuola Normale Superiore 56126 Pisa Italy QuEra Computing Boston Massachusetts 02143 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.

关键词： Machine learning Quantum algorithms Quantum computation Markovian processes Numerical techniques

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