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Quantum Reinforcement Learning for QoS-Aware real-time Job Scheduling in Cloud Systems

IEEE Systems Journal 2025年

作者： Dai, Shuhong Saurabh, Nishant Wang, Qingle Nian, Jiawei Kan, Shuwen Mao, Ying Cheng, Long North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources the School of Control and Computer Engineering Beijing102206 China Utrecht University Department of Information and Computing Sciences CS Utrecht 3584 Netherlands Fordham University Department of Computer and Information Science New YorkNY10458 United States

Effective cloud job scheduling is essential for enhancing the performance and operational efficiency of cloud-based services, directly impacting their quality of service (QoS). Among existing methodologies, deep reinforcement learning (DRL) has proven effective in addressing complex, multidimensional optimization challenges in real-time scheduling. With advancements in quantum computing, quantum neural networks (QNNs) are showing unique advantages in information representation and processing. This study is the first to explore quantum reinforcement learning (QRL) for real-time job scheduling in cloud systems. Specifically, we propose a QRL framework that utilizes variational and encoding layers to convert state information into quantum data, repeatedly embedded into a QNN to compute optimal value returns. This approach aims to enhance QoS by improving job execution success rates and reducing average response times with unpredictable job arrivals. We present the detailed design of our approach, and our simulation results demonstrate that the QRL method significantly exceeds established baselines, including those based on DRL, across a range of workload intensities and computational resource configurations. This is particularly evident under high-load conditions, where our approach can achieve 55.2% higher success rates, underscoring its significant potential in cloud job scheduling optimization. © 2007-2012 IEEE.

关键词： Qubits

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Multi-granularity ensemble sample selection and label correction for classification with noisy labels

引用

Applied Soft computing 2025年 180卷

作者： Kecan Cai Hongyun Zhang Witold Pedrycz Duoqian Miao Chaofan Chen Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkiye

Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.

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Deep neural network discrimination of multiplexed superconducting qubit states

arXiv

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arXiv 2021年

作者： Lienhard, Benjamin Vepsäläinen, Antti Govia, Luke C.G. Hoffer, Cole R. Qiu, Jack Y. Ristè, Diego Ware, Matthew Kim, David Winik, Roni Melville, Alexander Niedzielski, Bethany Yoder, Jonilyn Ribeill, Guilhem J. Ohki, Thomas A. Krovi, Hari K. Orlando, Terry P. Gustavsson, Simon Oliver, William D. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Engineering and Computing Group Raytheon BBN Technologies CambridgeMA02138 United States MIT Lincoln Laboratory LexingtonMA02421 United States

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multi-qubit systems. As system size increases, multiplexed qubit readout becomes a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multi-qubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully-connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25 % due to the compensation of system-dependent nonidealities such as readout crosstalk which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Skeleton-Based Pre-Training With Discrete Labels for Emotion Recognition in IoT Environments

引用

IEEE Internet of Things Journal 2025年

作者： Zhang, Zhen Liang, Feng Wang, Wei Zeng, Runhao Leung, Victor C.M Hu, Xiping Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen China Shenzhen MSU-BIT University Artificial Intelligence Research Institute China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Gansu China Beijing Institute of Technology School of Medical Technology China University of British Columbia Department of Electrical and Computer Engineering Canada

Self-supervised emotion recognition leveraging skeleton-based data offers a promising approach for classifying emotional expressions within the extensive amount of unlabeled data gathered by sensors in the Internet of Things (IoT). Recent advancements in this field have been driven by contrastive learning-based or generative learning-based self-supervised methods, which effectively tackle the issue of sparsely labeled data. In emotion recognition tasks, the emotional high-level semantics embedded in the skeleton data are more important than the subtle joint movements. Compared to existing methods, discrete label prediction can encourage SSL models to abstract high-level semantics in a manner similar to human perception. However, it is challenging to comprehensively capture emotional expressed in skeleton data solely from joint-based features. Moreover, emotional information conveyed through body movements may include redundant details that hinder the understanding of emotional expression. To overcome these challenges, we propose a novel discrete-label-based emotion recognition framework named the Appendage-Informed Redundancy-ignoring (AIR) discrete label framework. First, we introduce the Appendage-Skeleton Partitioning (ASP) module, which leverages limb movement data from the original skeleton to explore emotional expression. Next, we propose the Appendage-refined Multi-scale Discrete Label (AMDL) module, which transforms traditional self-supervised tasks into classification tasks. This design continuously extracts emotional semantics from skeleton data during pre-training, functioning similarly to predicting categories and subsequently classifying samples. To further reduce the nonessential information in skeleton data that may negatively impact the generation of accurate emotional categories, we propose the Appendage Label Refinement (ALR) module. It refines the generated categories by using the relationships between the skeleton and the various appendages obtai

关键词： Labeled data

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A Smart Roll Wear Check Scheme for Ensuring the Rolling Quality of Steel Plates 1

引用

4th International Conference on Smart computing and Communications, SmartCom 2019

作者： Zhang, Kui Zhou, Xiaohu He, Heng Wang, Yonghao Wang, Weihao Li, Huajian College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom

ISBN: (数字)9783030341398

ISBN: (纸本)9783030341381

Roll surface wear morphology directly affects the surface quality of steel plates and even affects the texture composition of plates and strip steel products. Using image processing methods to judge the wear state of a roll is low cost, easy to operate, and easy to realize an automatic smart data processing system. In this paper, we propose a Smart Roll Wear Check (SRWC) scheme for ensuring the rolling quality of steel plates. In the SRWC scheme, roll surface images in different wear stages are analyzed, from which seventeen dimension features are extracted. At the same time, the fractal theory is introduced to explore the relationship between fractal dimensions and roll wear degree. The results show that four characteristic parameters, such as roundness, equivalent area circle radius, second moment and texture entropy, and the fractal dimension can be used as effective parameters to quantitatively judge roll wear state. Lastly, a back-propagation (BP) neural network model for recognition and judgment for roll wear is established. It provides an experimental test to show that the five parameters as a quantitative evaluation for roll wear morphology are effective. By processing the data on the images, the SRWC scheme can demonstrate whether the roll needs to get off mill in time, so as to avoid the hidden danger of safety and ensure the rolling quality of the steel plate. © 2019, Springer Nature Switzerland AG.

关键词： Fractal dimension

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Optically Heralded Entanglement of Superconducting Systems in Quantum Networks

引用

Physical Review Letters 2021年第4期127卷 040503-040503页

作者： Stefan Krastanov Hamza Raniwala Jeffrey Holzgrafe Kurt Jacobs Marko Lončar Matthew J. Reagor Dirk R. Englund Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA U.S. Army Research Laboratory Computational and Information Sciences Directorate Adelphi Maryland 20783 USA Department of Physics University of Massachusetts at Boston Boston Massachusetts 02125 USA Rigetti Computing 775 Heinz Avenue Berkeley California 94710 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.

关键词： Entanglement production Optical & microwave phenomena Quantum entanglement Quantum networks Superconducting qubits

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A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System

arXiv

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arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

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SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

arXiv

引用

arXiv 2021年

作者： Liang, Xin Zhao, Kai Di, Sheng Li, Sihuan Underwood, Robert Gok, Ali M. Tian, Jiannan Deng, Junjing Calhoun, Jon C. Tao, Dingwen Chen, Zizhong Cappello, Franck Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Department of Computer Science & Engineering University of California Riverside RiversideCA92521 United States Facebook Inc. Menlo ParkCA94025 United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL60439 United States Cerebras Systems SunnyvaleCA94085 United States Department of Computer Science Washington State University PullmanWA99164 United States School of Computing Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Today’s scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compressor has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized/optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we develop a novel modular, composable compression framework (namely SZ3), which involves three significant contributions. (1) SZ3 features a modular abstraction for the prediction-based compression framework such that the new compression modules can be plugged in easily. (2) SZ3 supports multi algorithm predictors and can automatically select the best-fit predictor for each data block based on the designed error estimation criterion. (3) SZ3 allows users to easily compose different compression pipelines on demand, such that both compression quality and performance can be significantly improved for their specific datasets and requirements. (4) In addition, we evaluate several lossy compressors composed from SZ3 using the real-world datasets. Specifically, we leverage SZ3 to improve the compression quality and performance for different use-cases, including GAMESS quantum chemistry dataset and Advanced Photon Source (APS) instrument dataset. Experiments show that our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion compared with the state-of-the-art approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressors

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