با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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 |