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Efficient Measure for the Expressivity of Variational Quantum Algorithms

Physical Review Letters 2022年第8期128卷 080506-080506页

作者： Yuxuan Du Zhuozhuo Tu Xiao Yuan Dacheng Tao JD Explore Academy Beijing 101111 China School of Computer Science Faculty of Engineering The University of Sydney Darlington NSW 2008 Australia Center on Frontiers of Computing Studies Department of Computer Science Peking University Beijing 100871 China

The superiority of variational quantum algorithms (VQAs) such as quantum neural networks (QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity of the employed Ansätze. Namely, a simple Ansatz is insufficient to capture the optimal solution, while an intricate Ansatz leads to the hardness of trainability. Despite its fundamental importance, an effective strategy of measuring the expressivity of VQAs remains largely unknown. Here, we exploit an advanced tool in statistical learning theory, i.e., covering number, to study the expressivity of VQAs. Particularly, we first exhibit how the expressivity of VQAs with an arbitrary Ansätze is upper bounded by the number of quantum gates and the measurement observable. We next explore the expressivity of VQAs on near-term quantum chips, where the system noise is considered. We observe an exponential decay of the expressivity with increasing circuit depth. We also utilize the achieved expressivity to analyze the generalization of QNNs and the accuracy of VQE. We numerically verify our theory employing VQAs with different levels of expressivity. Our Letter opens the avenue for quantitative understanding of the expressivity of VQAs.

关键词： Learning theory Machine learning Quantum algorithms Quantum computation Resource theories

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems 1

Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memor...

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1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022

作者： Daymude, Joshua J. Richa, Andréa W. Scheideler, Christian Biodesign Center for Biocomputing Security and Society Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Department of Computer Science Universität Paderborn Germany

ISBN: (纸本)9783959772242

Mutual exclusion is a classical problem in distributed computing that provides isolation among concurrent action executions that may require access to the same shared resources. Inspired by algorithmic research on distributed systems of weakly capable entities whose connections change over time, we address the local mutual exclusion problem that tasks each node with acquiring exclusive locks for itself and the maximal subset of its "persistent" neighbors that remain connected to it over the time interval of the lock request. Using the established time-varying graphs model to capture adversarial topological changes, we propose and rigorously analyze a local mutual exclusion algorithm for nodes that are anonymous and communicate via asynchronous message passing. The algorithm satisfies mutual exclusion (non-intersecting lock sets) and lockout freedom (eventual success with probability 1) under both semi-synchronous and asynchronous concurrency. It requires O(∆) memory per node and messages of size Θ(1), where ∆ is the maximum number of connections per node. We conclude by describing how our algorithm can implement the pairwise interactions assumed by population protocols and the concurrency control operations assumed by the canonical amoebot model, demonstrating its utility in both passively and actively dynamic distributed systems. © Joshua J. Daymude, Andréa W. Richa, and Christian Scheideler;licensed under Creative Commons License CC-BY 4.0

关键词： Locks (fasteners)

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Feature Enhancer Segmentation Network (FES-Net)for Vessel Segmentation

Feature Enhancer Segmentation Network (FES-Net)for Vessel Se...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Tariq M. Khan Muhammad Arsalan Shahzaib Iqbal Imran Razzak Erik Meijering School of Computer Science & Engineering UNSW Sydney Australia KINDI Center For Computing Research Qatar University Doha Qatar Department of Electrical and Computer Engineering COMSATS University Islamabad (CUI) Islamabad Pakistan

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature.

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud computing

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Procedia computer science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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A simulation of a memory subsystem using a highly energy-efficient but erroneous MRAM 9

A simulation of a memory subsystem using a highly energy-eff...

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9th International Symposium on computing and Networking Workshops, CANDARW 2021

作者： Saito, Daiki Hirofuchi, Takahiro Arai, Hiroko Sato, Yukinori Toyohashi University of Technology Department of Computer Science And Engineering Aichi Japan Digital Architecture Research Center Tokyo Japan Research Center For Emerging Computing Technologies Tsukuba Japan

ISBN: (纸本)9781665428354

In order to reduce power consumption of the main memory of computers, the use of MRAM (magnetoresistive random access memory) instead of DRAM is attractive, since MRAM does not require periodic refresh energy while achieving sufficiently small read/write latencies. Among various types of MRAM devices, VC-MRAM (voltage-controlled MRAM) has a great potential to realize massively energy-efficient memory subsystems. Its write energy is 2 times orders of magnitudes smaller than that of STT-MRAM. The problem with VC-MRAM, however, is that its write error rate (i.e., 10-4 with today's typical technologies) is much higher than the acceptable error rate that has been supposed to be required for the main memory (i.e., 10-9). In this paper, we investigate whether such errors derived from an erroneous memory device cause a problem in actual program execution in the memory subsystem with hierarchical cache memory. We develop a memory subsystem simulator that mimics the structure of the multi-level caches and the main memory based on a VC-MRAM model, and observe cell-level operations in the main memory throughout the execution of benchmark programs. From results, we find the number of bit corruptions depends greatly on the cache size and the error model of the device. This is because the number of memory accesses itself can be significantly reduced by passing through the cache. Furthermore, we observe that the occurrences of bit corruptions more than 3 bits that cannot be handled properly by the hamming ECC codes seldom happen. This observation leads us to the insight that VC-MRAM with Hamming ECC can be used as main memory in some applications if these are executed on CPUs with a large cache memory. © 2021 IEEE.

关键词： MRAM devices

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Functional matrix product state simulation of continuous variable quantum circuits

arXiv

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arXiv 2025年

作者： Michelsen, Andreas Bock Marqversen, Frederik K. Kastoryano, Michael Department of Computer Science IT University of Copenhagen Copenhagen SDK-2300 Denmark Kvantify Aps Copenhagen SDK-2300 Denmark Department of Physics and Astronomy Aarhus University Aarhus CDK-8000 Denmark Department of Computer Science University of Copenhagen Copenhagen ØDK-2100 Denmark AWS Center for Quantum Computing PasadenaCA91125 United States

We introduce a functional matrix product state (FMPS) based method for simulating the real-space representation of continuous-variable (CV) quantum computation. This approach efficiently simulates non-Gaussian CV systems by leveraging their functional form. By addressing scaling bottlenecks, FMPS enables more efficient simulation of shallow, multi-mode CV quantum circuits with non-Gaussian input states. The method is validated by simulating random shallow and cascaded circuits with highly non-Gaussian input states, showing superior performance compared to existing techniques, also in the presence of loss. © 2025, CC BY.

关键词： Qubits

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A SURVEY ON COMPUTATIONAL SOLUTIONS FOR RECONSTRUCTING COMPLETE OBJECTS BY REASSEMBLING THEIR FRACTURED PARTS

arXiv

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arXiv 2024年

作者： Lu, Jiaxin Liang, Yongqing Han, Huijun Hua, Jiacheng Jiang, Junfeng Li, Xin Huang, Qixing Computer Science Department The University of Texas at Austin 2317 Speedway AustinTX United States Department of Computer Science and Engineering Texas A&M University 435 Nagle St College StationTX United States Department of Computer Science and Technology Tsinghua University 30 Shuangqing Road Beijing China College of Artificial Intelligence and Automation Hohai University 1915 Hohai Avenue Jiangsu Changzhou China Section of Visual Computing & Computational Media Department of Computer Science and Engineering Texas A&M University 789 Ross Street College StationTX United States

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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SVTR: Scene Text Recognition with a Single visual Model

arXiv

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arXiv 2022年

作者： Du, Yongkun Chen, Zhineng Jia, Caiyan Yin, Xiaoting Zheng, Tianlun Li, Chenxia Du, Yuning Jiang, Yu-Gang School of Computer and Information Technology Beijing Jiaotong University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University China Baidu Inc. China

Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://***/PaddlePaddle/PaddleOCR. © 2022, CC BY.

关键词： Mixing

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Design and Development of a Low-Cost Voice Interactive Children Educational Robot ‘TINY’ with Natural Language Processing

Design and Development of a Low-Cost Voice Interactive Child...

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Image Processing and Robotics (ICIP), International Conference on

作者： Nohori Jobaida Mohammad Shidujaman Ahsan Mahbub Hooman Samani Chinthaka Premachandra Mohammad Faisal Uddin Saadia Binte Alam Department of Computer Science and Engineering RIoT Center Independent University Bangladesh Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Department of Electronic Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

Educational robotics is to improve children’s and teenagers’ skill and competency development via the use of electronic components and robots in an interdisciplinary learning environment. It focuses mostly on the STEAM fields, while it can also touch on subjects like geography, history, and languages. STEAM education relates these 5 subjects those are science, Technology, Engineering, Arts and Mathematics. The capacity to which children could interact with a social robot in a novel learning environment is being studied. A method for measuring how actively children with learning difficulties participate in adequately prepared activities is described, utilizing multimodal machine learning. The goal is to find the child’s sustained attention throughout the conversation. To achieve this goal, visual and aural data are recorded between the kid and the robot. Education robots for kids are emerging as tools for learning which can enhance young children’s early reading and language skills considering recent technical developments. Social robots are those that can interact with humans on a mental and social level. The most recent studies on social robots and how typically developing kids, aged 0 to 8, pick up early language and literacy abilities have been examined. For the entirety of the study, five main themes were identified. Our designed robot TINY, a theoretical foundation for social robotic education, which stands for Teach, Inspire, Nurture, and Yield. Research has shown that social robots can facilitate early language acquisition, a thorough study has been done on the formation of early reading abilities. Additionally, our robot explores the potential of low-cost support for early language acquisition and literacy development, aiming to provide access to high-quality educational tools for all students.

关键词： visualization Social robots Education Oral communication Machine learning Particle measurements Natural language processing

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