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DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022 Quantum routing plays a key role in the development of the next-generation network system. In
particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping
among particles (e.g., photons) associated with nodes in the network. From another side of computing,
machine learning has achieved numerous breakthrough successes in various application domains, including
networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum
networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model
for quantum networks that employs machine learning architectures to construct the routing path for the
maximum number of demands (source–destination pairs) within a time window. Specifically, we present a
deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA
utilizes an empirically designed deep neural network that observes the current network states to accommodate
the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training
process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated
requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a
rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in
extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the
model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum
networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks. |
مقاله انگلیسی |
2 |
EntangleNetSat: A Satellite-Based Entanglement Resupply Network
-2022 In the practical context of quantum networks, quantum teleportation plays a key role in
transmitting quantum information. In the process of teleportation, a maximally entangled pair is consumed.
Through this paper, an efficient scheme of re-establishing entanglement between different nodes in a
quantum network is explored. A hybrid land-satellite network is considered, where the land-based links
are used for short-range communication, and the satellite links are used for transmissions between distant
nodes. This new scheme explores many different possibilities of resupplying the land nodes with entangled
pairs, depending on: the position of the satellites, the number of pairs available and the distance between
the nodes themselves. As to make the entire process as efficient as possible, we consider the situations of
direct transmissions of entangled photons and also the transmissions making use of entanglement swapping.
An analysis is presented for concrete scenarios, sustained by numerical data.
INDEX TERMS: Quantum communication | entanglement | teleportation | entanglement swapping | routing scheme | satellite. |
مقاله انگلیسی |
3 |
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 |
4 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
5 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |
6 |
Design of robot automatic navigation under computer intelligent algorithm and machine vision
طراحی ربات ناوبری خودکار تحت الگوریتم هوشمند کامپیوتر و بینایی ماشین-2022 This work aims to explore the robot automatic navigation model under computer intelligent algorithms and
machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high
cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image
processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low ef-
ficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-
based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox
Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The perfor-
mance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant
colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further
analysis of the proposed SSD neural network through comparison with other neural networks suggests that its
feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network
algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the
constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise
of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later
promotion and adoption of mobile robots in various fields. keywords: الگوریتم هوش کامپیوتری | بینایی ماشین | ربات | ناوبری خودکار | الگوریتم کلونی مورچه ها | Computer intelligence algorithm | Machine vision | Robot | Automatic navigation | Ant colony algorithm |
مقاله انگلیسی |
7 |
تکنیک ها و کاربردهای توالی یابی RNA تک سلولی در تحقیقات تکوین تخمدان و بیماری های مرتبط
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 23 تخمدان یک ارگان بسیار سازمان یافته متشکل از سلول های زایا و انواع مختلف سلول های سوماتیک است که ارتباطات آنها منجر به تکوین تخمدان و تولید تخمک های عملکردی می شود. تفاوت بین سلول های منفرد ممکن است اثرات عمیقی بر عملکرد تخمدان داشته باشد. تکنیکهای توالییابی RNA تک سلولی، رویکردهای امیدوارکنندهای برای کشف ترکیب انواع سلولی ارگانیسم ها، پویایی رونوشتها یا ترنسکریپتوم، شبکه تنظیمکننده بین ژنها و مسیرهای سیگنالدهی بین انواع سلولها در وضوح تک سلولی هستند. در این مطالعه، ما یک مرور کلی از تکنیکهای توالییابی RNA تک سلولی موجود از جمله Smart-seq2 و Drop-seq و همچنین کاربردهای آنها در تحقیقات بیولوژیکی و بالینی ارائه میکنیم تا درک بهتری از مکانیسمهای مولکولی زیربنای تکوین تخمدان و بیماری های مرتبط با آن ارائه کنیم.
کلیدواژگان: تکوین تخمدانن | توالی یابی RNA تک سلولی | شبکه تنظیمی | بیماری ها |
مقاله ترجمه شده |
8 |
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022 Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having
applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for
optimally routing transportation of goods by vehicles at a given
set of locations. This paper discusses the broader problem of
vehicle traffic management, more popularly known as the Vehicle
Routing Problem (VRP), and investigates the possible use of
near-term quantum devices for solving it. For this purpose,
we give the Ising formulation for VRP and some of its constrained
variants. Then, we present a detailed procedure to solve VRP
by minimizing its corresponding Ising Hamiltonian using a
hybrid quantum-classical heuristic called Quantum Approximate
Optimization Algorithm (QAOA), implemented on the IBM
Qiskit platform. We compare the performance of QAOA with
classical solvers such as CPLEX on problem instances of up to
15 qubits. We find that performance of QAOA has a multifaceted
dependence on the classical optimization routine used, the depth
of the ansatz parameterized by p, initialization of variational
parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms. |
مقاله انگلیسی |
9 |
Timing and Resource-Aware Mapping of Quantum Circuits to Superconducting Processors
نگاشت زمان بندی و آگاهی از منابع مدارهای کوانتومی به پردازنده های ابررسانا-2022 Quantum algorithms need to be compiled to respect
the constraints imposed by quantum processors, which is known
as the mapping problem. The mapping procedure will result in an
increase of the number of gates and of the circuit latency, decreasing the algorithm’s success rate. It is crucial to minimize mapping
overhead, especially for noisy intermediate-scale quantum (NISQ)
processors that have relatively short qubit coherence times and
high gate error rates. Most of prior mapping algorithms have only
considered constraints, such as the primitive gate set and qubit
connectivity, but the actual gate duration and the restrictions
imposed by the use of shared classical control electronics have
not been taken into account. In this article, we present a mapper
called Qmap to make quantum circuits executable on scalable
processors with the objective of achieving the shortest circuit
latency. In particular, we propose an approach to formulate the
classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore,
we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations
that minimally extends circuit latency. To analyze the mapping
overhead and evaluate the performance of different mappers, we
map 56 quantum benchmarks onto a superconducting processor named Surface-17. Compared to a prior mapping strategy
that minimizes the number of operations, Qmap can reduce the
latency overhead (LtyOH) up to 47.3% and operation overhead
up to 28.6%, respectively.
Index Terms—Quantum compilation | quantum computing | resource-constrained scheduling | routing. |
مقاله انگلیسی |
10 |
Performance evaluation of Focused Beam Routing for IoT applications in underwater environment
ارزیابی عملکرد مسیریابی پرتو متمرکز برای کاربردهای اینترنت اشیا در محیط زیر آب-2022 Underwater applications are becoming more and more interesting to industry and academy.
They include data gathering for human safety and environment monitoring, control of underwater robots for various tasks and so on. Because of the accessibility limitations in underwater
environment, applications tend to be automated and delay tolerant. In this paper, we consider
IoT applications in underwater environment, while using Delay Tolerant Networking (DTN)
carry–store–forwarding paradigm. DTN routing protocols are used to forward data from the
monitoring mobile sensors to collecting devices at the water surface and vice-versa. One
characteristic of routing protocols for DTN is flooding of messages to increase the delivery
probability. For instance, Epidemic Routing (ER) protocol creates a copy of each message for
each new node that does not already have the message in its memory. This increases the
probability of delivery, but on the other hand, creates overhead in each node’s buffer, and uses
a lot of valuable energy from the forwarding and receiving nodes. This work aims to analyze by
simulations the performance of Focused Beam Routing (FBR) protocol for different FBR angles
and different applications. We use Delivery Probability, Average Number of Hops, Overhead
Ratio and Buffer Occupancy to simulate our scenarios by The ONE simulator. Simulation results
show that for narrow angles of FBR the performance is better. In case of FBR-45, average
hop count and overhead ratio are decreased by 10.9% and 16.6% respectively, compared to
FBR-180. However, delivery probability decreases by only 3.9%.
Keywords: Underwater environment | Delay tolerant network | DTN | Focused Beam Routing | FBR the ONE simulator |
مقاله انگلیسی |