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نتیجه جستجو - Optimization

تعداد مقالات یافته شده: 1045
ردیف عنوان نوع
1 Effects of Dynamical Decoupling and Pulse-Level Optimizations on IBM Quantum Computers
اثرات جداسازی دینامیکی و بهینه سازی سطح پالس بر روی کامپیوترهای کوانتومی IBM-2022
Currently available quantum computers are prone to errors. Circuit optimization and error mitigation methods are needed to design quantum circuits to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling (DD) is generally used to suppress the decoherence error, and different DD strategies have been proposed. Moreover, the circuit fidelity can be improved by pulse-level optimization, such as creating hardware-native pulse-efficient gates. This article implements all the popular DD sequences and evaluates their performances on IBM quantum chips with different characteristics for various wellknown quantum applications. Also, we investigate combining DD with the pulse-level optimization method and apply them to QAOA to solve the max-cut problem. Based on the experimental results, we find that DD can be a benefit for only certain types of quantum algorithms, while the combination of DD and pulse-level optimization methods always has a positive impact. Finally, we provide several guidelines for users to learn how to use these noise mitigation methods to build circuits for quantum applications with high fidelity on IBM quantum computers.
INDEX TERMS: Error mitigation | noisy intermediate-scale quantum (NISQ) hardware.
مقاله انگلیسی
2 Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سخت‌افزار-نرم‌افزار روی تراشه مبتنی بر شبکه‌های عصبی عمیق برای کاربرد بینایی ماشین-2022
Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial intelligence (AI) in vision applications that make these systems intelligent and able to make decisions close to or similar to humans. In this context, the AI’s integration on embedded systems poses many challenges, given that its performance depends on data volume and quality they assimilate to learn and improve. This returns to the energy consumption and cost constraints of the FPGA-SoC that have limited processing, memory, and communication capacity. Despite this, the AI algorithm implementation on embedded systems can drastically reduce energy consumption and processing times, while reducing the costs and risks associated with data transmission. Therefore, its efficiency and reliability always depend on the designed prototypes. Within this range, this work proposes two different designs for the Traffic Sign Recognition (TSR) application based on the convolutional neural network (CNN) model, followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s, there is room for improvement using programmable logic (PL) and processing system (PS) in a hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers will be accelerated to provide a hardware accelerator for each layer where direct memory access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased hardware cost, and execution time optimization of 2.13 s, but, there was still room for design optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated to be a single computation engine where BRAM interface is used. The implementation results prove that our proposed embedded TSR design achieves the best performances compared to the first proposed architectures, in terms of execution time of about 0.03 s, computation roof of about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1
مقاله انگلیسی
3 Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022
Computer vision systems perform based on their design and parameter setting. In computer vision systems that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of the systems in terms of both results quality and computational costs. Appropriate setting of the weights for the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the system and subsystem to small changes in the distribution of data in a color space of the processed images. We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies. The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion
مقاله انگلیسی
4 Equivalence Checking of Quantum Circuits With the ZX-Calculus
بررسی هم ارزی مدارهای کوانتومی با ZX-calculus-2022
As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for automated methods to design and test potential applications rises. Equivalence checking of quantum circuits is an important, yet hardly automated, task in the development of the quantum software stack. Recently, new methods have been proposed that tackle this problem from widely different perspectives. One of them is based on the ZX-calculus, a graphical rewriting system for quantum computing. However, the power and capability of this equivalence checking method has barely been explored. The aim of this work is to evaluate the ZX-calculus as a tool for equivalence checking of quantum circuits. To this end, it is demonstrated how the ZX-calculus based approach for equivalence checking can be expanded in order to verify the results of compilation flows and optimizations on quantum circuits. It is also shown that the ZX-calculus based method is not complete—especially for quantum circuits with ancillary qubits. In order to properly evaluate the proposed method, we conduct a detailed case study by comparing it to two other state-of-the-art methods for equivalence checking: one based on path-sums and another based on decision diagrams. The proposed methods have been integrated into the publicly available QCEC tool (https://github.com/cda-tum/qcec) which is part of the Munich Quantum Toolkit (MQT).
Index Terms: Quantum computing | formal verification | quantum circuit.
مقاله انگلیسی
5 Exploring Potential Applications of Quantum Computing in Transportation Modelling
بررسی کاربردهای بالقوه محاسبات کوانتومی در مدل سازی حمل و نقل-2022
The idea that quantum effects could be harnessed to allow faster computation was first proposed by Feynman. As of 2020 we appear to have achieved ‘quantum supremacy’, that is, a quantum computer that performs a given task faster than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum computing to transportation simulation and planning. To date, no such research was found to exist, therefore we begin with an introduction to quantum computing for the programmers of transport models. We discuss existing quantum computing research relevant to transportation, finding developments in network analysis, shortest path computation, multi-objective routing, optimization and calibration – of which the latter three appear to offer the greater promise in future research. Two examples are developed in greater detail, (1) an application of Grover’s quantum algorithm for extracting the mean, which has general applicability towards summarizing distributions which are expensive to compute classically, is applied to an assignment or betweenness model - quantum speedup is elusive in the general case but achievable when trading speed for accuracy for limited outputs; (2) quantum optimization is applied to an activity-based model, giving a theoretically quadratic speedup. Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future remain a challenge due to space overheads imposed by the requirement for reversible computation.
Index Terms: Quantum computing | assignment | betweenness | flows, activity models | tour models.
مقاله انگلیسی
6 Fuzzy Logic on Quantum Annealers
منطق فازی در آنیل های کوانتومی-2022
Quantum computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement, and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as Big Data, where computational efficiency represents a nonnegligible constraint to be taken into account. In order to pave the way toward this innovative scenario, this article introduces a novel representation of fuzzy sets and operators based on quadratic unconstrained binary optimization problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.
Index Terms: Fuzzy logic | quantum computing | simulated annealing.
مقاله انگلیسی
7 Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling
تکنیک‌های بهینه‌سازی ترکیبی کلاسیک-کوانتومی برای حل مسائل برنامه‌نویسی عدد صحیح مختلط در زمان‌بندی تولید-2022
Quantum computing (QC) holds great promise to open up a new era of computing and has been receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing the current quantum computers to complement classical techniques for solving real-world problems is of utmost importance. In this article, we develop QC-based solution strategies that exploit quantum annealing and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems. The applications of the proposed algorithms are illustrated through two case studies in production scheduling. First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multipurpose batch scheduling problem with a fractional objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf optimization solvers.
INDEX TERMS: Hybrid techniques | optimization | quantum annealing | quantum computing (QC) | scheduling.
مقاله انگلیسی
8 Implementation of Quantum Annealing: A Systematic Review
پیاده سازی آنیل کوانتومی: مروری سیستماتیک-2022
Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.
INDEX TERMS: Quantum annealing | implementation | review.
مقاله انگلیسی
9 Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
پیاده سازی انتخاب ویژگی گراف-نظری توسط الگوریتم بهینه سازی تقریبی کوانتومی-2022
Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
Index Terms: Feature selection | graph theory | parameterized quantum circuit | quantum approximation optimization algorithm | quantum computing.
مقاله انگلیسی
10 Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمان‌بندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینه‌سازی ترکیبی توسط محاسبات کوانتومی-2022
Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in 6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem, we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing. This will lead to the optimization of the entire system at a high speed and low power consumption in the future. Using computer simulation, we clarified that the proposed method realizes a more efficient communication compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization
مقاله انگلیسی
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