SPEEDUP

作品数:52被引量:98H指数:4
导出分析报告
相关领域:自动化与计算机技术更多>>
相关作者:王鼎兴刘德才王丙申沈复陈志奎更多>>
相关机构:清华大学中国石油大学(北京)中国石化兰州大学更多>>
相关期刊:《Communications in Theoretical Physics》《Journal of Beijing Institute of Technology》《Frontiers of Computer Science》《Wuhan University Journal of Natural Sciences》更多>>
相关基金:国家自然科学基金广东省自然科学基金国家高技术研究发展计划天津市自然科学基金更多>>
-

检索结果分析

结果分析中...
条 记 录,以下是1-10
视图:
排序:
Quantum advantages for image filtering on images with efficient encoding and lower-bounded signal-to-noise ratio
《Science China(Physics,Mechanics & Astronomy)》2024年第9期2-12,共11页Zidong Cui Shan Jin Akira Sone Xiaoting Wang 
supported by the National Natural Science Foundation of China(Grant No.92265208);the National Key R&D Program of China(Grant No.2018YFA0306703);startup funding supported by the University of Massachusetts,Boston。
Quantum image processing has long been a fascinating field,but establishing the existence of quantum speedup for all images remains challenging.In this study,we aim to identify a subset of images for which a quantum a...
关键词:quantum computing quantum image filtering quantum speedup 
Advancements in quantum image filtering:Exponential speedup on a subset of images
《Science China(Physics,Mechanics & Astronomy)》2024年第9期13-13,共1页Lvzhou Li 
The pursuit of quantum supremacy in computational tasks has driven the exploration of quantum algorithms capable of surpassing classical counterparts.In the realm of image processing,a notable advancement towards this...
关键词:QUANTUM SUBSET FILTERING 
Quantum speedup and non-Markovianity of an atom in structured reservoirs:pseudomodes as a good description of environmental memory
《Communications in Theoretical Physics》2024年第8期60-66,共7页Maryam Hadipour Soroush Haseli Saeed Haddadi 
supported by Semnan University under Contract No. 21270。
Following the recent paper(Teittinen et al 2019 New J. Phys. 21 123041), one can see that in general there is no simple relation between non-Markovianity and quantum speed limit. Here, we investigate the connection be...
关键词:quantum speed limit non-Markovianity pseudomode method structured environments 
Quantum speedup and limitations on matroid property problems
《Frontiers of Computer Science》2024年第4期189-196,共8页Xiaowei HUANG Jingquan LUO Lvzhou LI 
National Natural Science Foundation of China(Grant Nos.62272492,61772565);Guangdong Basic and Applied Basic Research Foundation(No.2020B1515020050).
Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so on.On the other hand,quantum computing has attracted much att...
关键词:quantum computing MATROID quantum algorithm quantum query complexity 
SSL Depth: self-supervised learning enables 16× speedup in confocal microscopy-based 3D surface imaging [Invited]被引量:1
《Chinese Optics Letters》2024年第6期3-7,共5页Ze-Hao Wang Tong-Tian Weng Xiang-Dong Chen Li Zhao Fang-Wen Sun 
supported by the Innovation Program for Quantum Science and Technology (No.2021ZD0303200);the CAS Project for Young Scientists in Basic Research (No.YSBR-049);the National Natural Science Foundation of China (No.62225506);the Anhui Provincial Key Research and Development Plan (No.2022b13020006)。
In scientific and industrial research, three-dimensional (3D) imaging, or depth measurement, is a critical tool that provides detailed insight into surface properties. Confocal microscopy, known for its precision in s...
关键词:confocal microscopy 3D surface imaging self-supervised learning 
A Distributed Ant Colony Optimization Applied in Edge Detection
《Journal of Computer and Communications》2024年第8期161-173,共13页Min Chen 
With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, le...
关键词:Distributed System Ant Colony Optimization Edge Detection MAPREDUCE SPEEDUP 
Correlated optical convolutional neural network with“quantum speedup”
《Light(Science & Applications)》2024年第2期333-346,共14页Yifan Sun Qian Li Ling-Jun Kong Xiangdong Zhang 
National key R&D Program of China(2022YFA1404904);National Natural Science Foundation of China(12234004);National Natural Science Foundation of China(No.11904022).
Compared with electrical neural networks,optical neural networks(ONNs)have the potentials to break the limit of the bandwidth and reduce the consumption of energy,and therefore draw much attention in recent years.By f...
关键词:PROCESS QUANTUM CONVOLUTION 
Quantum dynamical speedup for correlated initial states被引量:1
《Communications in Theoretical Physics》2023年第7期95-102,共8页Alireza Gholizadeh Maryam Hadipour Soroush Haseli Saeed Haddadi Hazhir Dolatkhah 
The maximal evolution speed of any quantum system can be expressed by the quantum speed limit time.In this paper,we consider a model in which the system has a correlation with the environment.The influence of the init...
关键词:quantum speed limit non-Markovianity correlated initial state quantum coherence 
The Memory-Bounded Speedup Model and Its Impacts in Computing
《Journal of Computer Science & Technology》2023年第1期64-79,共16页孙贤和 鲁潇阳 
supported in part by the U.S.National Science Foundation under Grant Nos.CCF-2029014 and CCF-2008907.
With the surge of big data applications and the worsening of the memory-wall problem,the memory system,instead of the computing unit,becomes the commonly recognized major concern of computing.However,this“memorycent...
关键词:memory-bounded speedup scalable computing memory-wall performance modeling and optimization data-centric design 
Adventures Beyond Amdahl's Law:How Power-Performance Measurement and Modeling at Scale Drive Server and Supercomputer Design
《Journal of Computer Science & Technology》2023年第1期80-86,共7页Kirk W.Cameron 
Amdahl’s Law painted a bleak picture for large-scale computing.The implication was that parallelism was limited and therefore so was potential speedup.While Amdahl’s contribution was seminal and important,it drove o...
关键词:Amdahl’s Law SPEEDUP power-aware computing power modeling performance modeling performance prediction power measurement 
检索报告 对象比较 聚类工具 使用帮助 返回顶部