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arXiv 2024年

作者： Liu, Jie Wang, Xin Department of Physics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute Guangdong Shenzhen518057 China

Quantum Approximate Optimization Algorithm (QAOA) is one of the fundamental variational quantum algorithms, while a version of QAOA that includes counterdiabatic driving, termed Digitized Counterdiabatic QAOA (DC-QAOA), is generally considered to outperform QAOA for all system sizes when the circuit depth for the two algorithms are held equal. Nevertheless, DC-QAOA introduces more CNOT gates per layer, so the overall circuit complexity is a tradeoff between the number of CNOT gates per layer and the circuit depth, and must therefore be carefully assessed. In this paper, we conduct a comprehensive comparison of DC-QAOA and QAOA on MaxCut problem with the total number of CNOT gates held equal, and we focus on one implementation of counterdiabatic terms using nested commutators in DC-QAOA, termed as DC-QAOA(NC). We have found that DC-QAOA(NC) reduces the overall circuit complexity as compared to QAOA only for sufficiently large problems, and for MaxCut problem the number of qubits must exceed 16 for DC-QAOA(NC) to outperform QAOA. We have further shown that this advantage can be understood from the effective dimensions introduced by the counterdiabatic driving terms. Moreover, based on our finding that the optimal parameters generated by DC-QAOA(NC) strongly concentrate in the parameter space, we haved devised an instance-sequential training method for DC-QAOA(NC) circuits, which, compared to traditional methods, offers performance improvement while using even fewer quantum resources. Our findings provide a more comprehensive understanding of the advantages of DC-QAOA circuits and an efficient training method based on their generalizability. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence

arXiv

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arXiv 2024年

作者： Yuan, Xin Li, Ning Chen, Quan Xu, Wenchao Zhang, Zhaoxin Guo, Song

Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligence (EI), in which the AI model is divided into different sub-models and the resource-intensive sub-model is offloaded to edge server wirelessly for reducing resource requirements and inference latency. However, the previous works mainly concentrate on improving and optimizing the system's QoS, ignore the effect of QoE which is another critical item for the users except for QoS. Even the QoE has been widely learned in EC, considering the differences between task offloading in EC and split inference in EI, and the specific issues in QoE which are still not addressed in EC and EI, these algorithms cannot work effectively in edge split inference scenarios. Thus, an effective resource allocation algorithm is proposed in this paper, for 1) accelerating split inference in EI and 2) achieving the tradeoff between inference delay, QoE, and resource consumption, abbreviated as ERA. Specifically, the ERA takes the resource consumption, QoE, and inference latency into account to find the optimal model split strategy and resource allocation strategy. Since the minimum inference delay and resource consumption, and maximum QoE cannot be satisfied simultaneously, the gradient descent (GD) based algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is developed to reduce the complexity of the GD algorithm caused by parameter discretization. Additionally, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation error. The experimental results demonstrate that the performance of ERA is much better than that of the previous studies. © 2024, CC BY.

关键词： approximation algorithms

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OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA Algorithm

OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA...

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Chinese Control and Decision Conference, CCDC

作者： Kaicheng Hao Yongli Yang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the power allocation problem in the downlink of an Orthogonal Time Frequency Space-Non-Orthogonal Multiple Access (OTFS-NOMA) system under high-speed conditions, with the objective of maximizing system capacity and rate. A novel Butterfly Optimization Algorithm based on Differential Evolution (DE) and Simulated Annealing (SA), referred to as SADEBOA, is proposed. In the global search phase, SADEBOA incorporates DE to enhance the global search capability of the Butterfly Optimization Algorithm (BOA), mutating the optimal individual and guiding the population towards the optimal individual's location. In the local search phase, SADEBOA combines SA to accept a local optimal solution as a new solution with a certain probability, introducing a certain bias that is more likely to approach the optimal solution. Comparative simulations are conducted with BOA, MSBOA, DA, and RSO algorithms, as well as traditional power allocation schemes. The simulation results demonstrate that, compared to the baseline BOA algorithm, MSBOA, DA, and RSO algorithms, SADEBOA achieves performance improvements of approximately 15%, 5.3%, 21.2%, and 28.4%, in enhancing OTFS-NOMA system capacity.

关键词： Time-frequency analysis Simulation Sociology Simulated annealing approximation algorithms Downlink Resource management

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Formal stabilization of a coupled ODE-PDE switched system

Formal stabilization of a coupled ODE-PDE switched system

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IEEE Conference on Decision and Control

作者： Adrien Le Coënt Jonathan Vacher Kenan Kergrene Univ Paris Est Creteil LACL Creteil France MAP5 Université Paris Cité CNRS Paris France Université de Technologie de Compiègne Laboratoire Roberval CS 60319 Compiègne Cedex France

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Partial Differential Equations (PDEs) are a ubiquitous model that describes a wide range of dynamical systems. While stabilization of systems involving PDEs is an important problem, little work has been done on formal verification and synthesis for such systems. In this paper, we explain how a coupled ODE-PDE control problem can be formally stabilized using a tiling based control synthesis algorithm associated to set based reachability, which is usually used on finite dimensional problems. To formally prove the stabilization of the PDE, the original infinite dimensional problem is transformed into a finite dimensional one using, among other tools, model order reduction. The strength of our approach relies on the fact that we never explicitly discretize the PDE state using e.g. a finite element approximation, and consequently, we provide stability guarantees directly on the infinite dimensional state.

关键词： Switched systems Partial differential equations Heuristic algorithms approximation algorithms Stability analysis Reduced order systems Mathematical models Finite element analysis Dynamical systems Formal verification

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Research on Data Mining Technology Based on Data Stream Management Systems

Research on Data Mining Technology Based on Data Stream Mana...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Shengyuan Zhang Baiwei Sun Taiying Chen Cornell University Manhattan New York USA University of California Irvine (UCI) California USA New York University New York USA

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the advent of the big data era, Data Stream Management Systems (DSMS) have gradually become important tools for processing massive real-time data. This paper studies the data mining technology within DSMS, exploring its applications in real-time data processing, anomaly detection, and decision support. First, the paper reviews the basic concepts and current status of DSMS and data mining technology. Then, it analyzes the challenges faced in data mining in a data stream environment and proposes improvement schemes. Through experimental verification, the improved algorithms proposed in this study show significant advantages in processing efficiency and accuracy. Finally, the paper summarizes the research results and points out directions for future research.

关键词： Accuracy Heuristic algorithms Data preprocessing Clustering algorithms Feature extraction approximation algorithms Real-time systems Classification algorithms Data mining Decision trees

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A Time-Fractional Optimal Transport: Formulation And Algorithm

SSRN

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SSRN 2024年

作者： Li, Yiqun Wang, Hong Li, Wuchen Department of Mathematics University of South Carolina ColumbiaSC29208 United States

The time-fractional optimal transport (OT) models are developed to describe the anomalous transport of the agents such that their densities are transported from the initial density distribution to the terminal one with the minimal *** general-proximal primal-dual hybrid gradient (G-prox PDHG) algorithm is applied to solve the OT formulations, in which a preconditioner induced by the numerical approximation to the time-fractional PDE is derived to accelerate the convergence of the algorithm for both problems. Numerical experiments for OT problems between Gaussian distributions are carried out to investigate the performance of the OT formulations. Those numerical experiments also demonstrate the effectiveness and flexibility of our proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Variational Quantum Factorization (VQF) using Different Variations of Quantum Approximate Optimization Algorithm (QAOA)

Variational Quantum Factorization (VQF) using Different Vari...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Janhavi Hatwar Nakul Aggarwal Debanjan Bhowmik Department of Physics Indian Institute of Science Education and Research Bhopal Madhya Pradesh India Department of Physics and Astronomy Rutgers University Piscataway New Jersey USA Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai Maharashtra

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Variational quantum factoring (VQF), a variational quantum algorithm, has been proposed as an alternative to Shor’s algorithm for factoring numbers and has been experimentally implemented to factor numbers much larger than what Shor’s algorithm has factorized thus far. The focus of this work is comparing the performance of different variations of quantum approximate optimization algorithm (QAOA) in terms of implementing VQF. Hence, VQF has been implemented here on the Qiskit quantum simulation framework using standard QAOA, multi-angle QAOA (maQAOA), conditional value at risk (CVaR) QAOA, and CVar maQAOA. Using these methods, VQF has been implemented for factorizing bi-prime numbers 143 and 291311 using 4 and 6 qubits respectively, and for factorizing a tri-prime number, 175, using 3 qubits. For factoring of 143 and 175, maQAOA yields the highest success probability numbers (0.92 and 0.99 respectively), while for factoring 291311, CVar maQAOA with 0.75 of the total measurements contributing to the expectation value yields the highest success probability.

关键词： Quantum algorithm Communication systems Qubit approximation algorithms Mathematical models Hardware Standards Optimization Quantum simulation

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Open-Loop and Feedback Nash Trajectories for Competitive Racing with iLQGames

Open-Loop and Feedback Nash Trajectories for Competitive Rac...

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International Conference on Intelligent Transportation

作者： Matthias Rowold Alexander Langmann Boris Lohmann Johannes Betz Chair of Automatic Control TUM School of Engineering and Design Technical University of Munich Germany Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University of Munich Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Interaction-aware trajectory planning is crucial for closing the gap between autonomous racing cars and human racing drivers. Prior work has applied game theory as it provides equilibrium concepts for non-cooperative dynamic problems. With this contribution, we formulate racing as a dynamic game and employ a variant of iLQR-called iLQGames-to solve the game. iLQGames finds trajectories for all players that satisfy the equilibrium conditions for a linear-quadratic approximation of the game and has been previously applied in traffic scenarios. We analyze the algorithm's applicability for trajectory planning in racing scenarios and evaluate it based on interaction awareness, competitiveness, and safety. With the ability of iLQGames to solve for open-loop and feedback Nash equilibria, we compare the behavioral outcomes of the two equilibrium concepts in simple scenarios on a straight track section.

关键词： Trajectory planning Heuristic algorithms Games approximation algorithms Trajectory Safety Automobiles Game theory Intelligent transportation systems

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A Hybrid Index-Based Block Construction and Retrieval Algorithm for Efficient Data Retrieval on Blockchain

A Hybrid Index-Based Block Construction and Retrieval Algori...

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Computer Science and Blockchain (CCSB), International Conference on

作者： Lei Mu Minqiang Lv Shanshan Wang Hui Cao Xi'an Institute of Aeronautical Computing Technology Aviation Industry Corporation of China (AVIC) Xi'an China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Blockchain, as a decentralized, secure, programmable, traceable, and tamper-resistant distributed ledger technology, has witnessed rapid development in various fields in recent years. This paper addresses the need for rapid data retrieval on blockchain and proposes a block construction and retrieval algorithm based on hybrid indexing. The algorithm aims to alter the underlying structure of blocks by utilizing data structures such as Merkle trees, linked lists, and hashes to facilitate data retrieval. Building upon this, block construction and retrieval algorithms are introduced to ensure efficient retrieval of corresponding transactions, maintaining low time complexity.

关键词： Computer science Filters Distributed ledger Buildings approximation algorithms Data structures Blockchains Time complexity Optimization Indexing

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Approximating Closed Queueing Networks in Semi-Markov Random Environments

Approximating Closed Queueing Networks in Semi-Markov Random...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)

作者： Yaqi Zhou Matthew Sheldon Giuliano Casale Department of Computing Imperial College London UK

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Modeling queueing networks in random environments is a challenging problem that finds application in routing, reliability analysis, and evaluation of systems processing bursty workloads. In this paper, we introduce a method to evaluate closed queueing networks in random environments that can deal with non-Markovian transitions among environment stages and accurately approximate time-averaged performance metrics. Our technique supports both state-independent and state-dependent random environments, the latter allowing us to analyze challenging queueing network models with bursty service processes. Using simulation on representative case studies, we show that our technique provides low approximation errors. We further demonstrate the applicability of our result to a mobility case study demonstrating the applicability of the proposed approach.

关键词： Measurement Analytical models Computational modeling Switched mode power supplies approximation algorithms Routing Telecommunications Telecommunication network reliability Iterative methods Queueing analysis

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