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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 |