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تعداد مقالات یافته شده: 2596
ردیف عنوان نوع
1 Development of an Undergraduate Quantum Engineering Degree
توسعه یک مدرک کارشناسی مهندسی کوانتوم-2022
Quantum computing, communications, sensing, and simulations are radically transformative technologies, with great potential to impact industries and economies. Worldwide, national governments, industries, and universities are moving to create a new class of workforce—the Quantum Engineers. Demand for such engineers is predicted to be in the tens of thousands within a five-year timescale, far exceeding the rate at which the world’s universities can produce Ph.D. graduates in the discipline. How best to train this next generation of engineers is currently a matter of debate. Quantum mechanics—long a pillar of traditional physics undergraduate degrees—must now be merged with traditional engineering offerings. This article discusses the history, development, and the first year of operation of the world’s first undergraduate degree in quantum engineering to be grown out of an engineering curriculum. The main purpose of this article is to inform the wider discussion, now being held by many institutions worldwide, on how best to formally educate the Quantum Engineer.
INDEX TERMS: Degree | education | engineering | quantum | undergraduate.
مقاله انگلیسی
2 IoTracker: A probabilistic event tracking approach for data-intensive IoT Smart Applications
IoTracker: یک رویکرد ردیابی رویداد احتمالی برای برنامه‌های هوشمند اینترنت اشیا با داده های فشرده-2022
Smart Applications for cities, industry, farming and healthcare use Internet of Things (IoT) approaches to improve the general quality. A dependency on smart applications implies that any misbehavior may impact our society with varying criticality levels, from simple inconveniences to life-threatening dangers. One critical challenge in this area is to overcome the side effects caused by data loss due to failures in software, hardware, and communication systems, which may also affect data logging systems. Event traceability and auditing may be impaired when an application makes automated decisions and the operating log is incomplete. In an environment where many events happen automatically, an audit system must understand, validate, and find the root causes of eventual failures. This paper presents a probabilistic approach to track sequences of events even in the face of logging data loss using Bayesian networks. The results of the performance analysis with three smart application scenarios show that this approach is valid to track events in the face of incomplete data. Also, scenarios modeled with Bayesian subnets highlight a decreasing complexity due to this divide and conquer strategy that reduces the number of elements involved. Consequently, the results improve and also reveal the potential for further advancement.
Keywords: Smart applications | Event tracker | Probabilistic tracker | Bayesian networks
مقاله انگلیسی
3 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
4 ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022
Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using com- puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.
keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation
مقاله انگلیسی
5 Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry
تکامل محاسبات کوانتومی: مفاهیم مدیریت نظری و نوآوری برای صنعت کوانتومی در حال ظهور-2022
Quantum computing is a vital research field in science and technology. One of the fundamental questions hardly known is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies for economic, industrial, and social change. This study confronts the question here by applying methods of computational scientometrics for publication analyses to explain the structure and evolution of quantum computing research and technologies over a 30-year period. Results reveal that the evolution of quantum computing from 1990 to 2020 has a considerable average increase of connectivity in the network (growth of degree centrality measure), a moderate increase of the average influence of nodes on the flow between nodes (little growth of betweenness centrality measure), and a little reduction of the easiest access of each node to all other nodes (closeness centrality measure). This evolutionary dynamics is due to the increase in size and complexity of the network in quantum computing research over time. This study also suggests that the network of quantum computing has a transition from hardware to software research that supports accelerated evolution of technological pathways in quantum image processing, quantum machine learning, and quantum sensors. Theoretical implications of this study show the morphological evolution of the network in quantum computing from a symmetric to an asymmetric shape driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation management based on R&D investments in new technological directions of quantum computing having a high potential for growth and impact in science and markets.
Index Terms: Innovation management | quantum algorithms | quantum computing (QC) | quantum network | technological change | technological paradigm | technological trajectories.
مقاله انگلیسی
6 The application of computer vision systems in meat science and industry – A review
کاربرد سیستم های بینایی کامپیوتری در علم و صنعت گوشت – مروری-2022
Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisi- tion devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.
keywords: سیستم بینایی کامپیوتری | گوشت | محصولات گوشتی | لاشه | Computer vision system | Meat | Meat products | Carcass
مقاله انگلیسی
7 Hybrid CV-DV Quantum Communications and Quantum Networks
ارتباطات کوانتومی ترکیبی CV-DV و شبکه های کوانتومی-2022
Quantum information processing (QIP) opens new opportunities for high-performance computing, high-precision sensing, and secure communications. Among various QIP features, the entanglement is a unique one. To take full advantage of quantum resources, it will be necessary to interface quantum systems based on different encodings of information both discrete and continuous. The goal of this paper is to lay the groundwork for the development of a robust and efficient hybrid continuous variable-discrete variable (CV-DV) quantum network, enabling the distribution of a large number of entangled states over hybrid DV-CV multi-hop nodes in an arbitrary topology. The proposed hybrid quantum communication network (QCN) can serve as the backbone for a future quantum Internet, thus providing extensive longterm impacts on the economy and national security through QIP, distributed quantum computing, quantum networking, and distributed quantum sensing. By employing the photon addition and photon subtraction modules we describe how to generate the hybrid DV-CV entangled states and how to implement their teleportation and entanglement swapping through entangling measurements. We then describe how to extend the transmission distance between nodes in hybrid QCN by employing macroscopic light states, noiseless amplification, and reconfigurable quantum LDPC coding. We further describe how to enable quantum networking and distributed quantum computing by employing the deterministic cluster state concept introduced here. Finally, we describe how the proposed hybrid CV-DV states can be used in an entanglement-based hybrid QKD.
INDEX TERMS: Entanglement | photon addition | photon subtraction | hybrid CV-DV entangled states | teleportation | entanglement swapping | entanglement distribution | hybrid quantum communication networks | entanglement-based hybrid QKD.
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 A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022
Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm the presence of this virus, some radiological investigations find some important features from the CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful to automatically process the CT scan images without any manual annotation and helpful in the easy interpretation. The proposed approach is based on artificial cell swarm optimization and will be known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented in the Matlab environment. The proposed approach uses a novel superpixel computation method which is helpful to effectively represent the pixel intensity information which is beneficial for the optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm optimization approach. So, a twofold contribution can be observed in this work which is helpful to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical impact of this work. Both qualitative and quantitative experimental results show the effectiveness of the proposed approach and also establish it as an effective computer-aided tool to fight against the COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β index are used to quantify the segmented results and it is observed that the proposed approach not only performs well but also outperforms some of the standard approaches. On average, the proposed approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and 9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization
مقاله انگلیسی
10 Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
روش ترکیبی کوانتومی-کلاسیک برای تشخیص تقلب با انتخاب ویژگی کوانتومی-2022
This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine’s feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum-based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.
INDEX TERMS: Fraud Detection | Quantum | Feature Selection | QSVM | Quantum Kernel Alignment
مقاله انگلیسی
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