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1 |
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 |
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
لایه VQE: یک رویکرد متغیر برای بهینه سازی ترکیبی در کامپیوترهای کوانتومی پر سر و صدا-2022 Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or
error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative
layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to
40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate
quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we
introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate
optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while
requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has
a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results
show that L-VQE performs well under realistic hardware noise.
INDEX TERMS: Combinatorial optimization | hybrid quantum-classical algorithm | quantum optimization. |
مقاله انگلیسی |
3 |
Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
یادگیری ماشینی الهام گرفته از کوانتومی برای 6G: مبانی، امنیت، تخصیص منابع، چالشها و دستورالعملهای تحقیقاتی آینده-2022 Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been
a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and
data structures. Hence, the conventional machine learning approaches in data training and processing have
exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly
dynamic applications and services. In this regard, the fast developing quantum computing with machine
learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the
processing efficiency and exponentially computational speed-up for effective quantum data representation
and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview
of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine
learning applications for 6G networks in terms of resource allocation and network security, considering their
enabling technologies and potential challenges. Finally, some dominating research issues and future research
directions for the quantum-inspired machine learning in 6G networks are elaborated.
INDEX TERMS: 6G networks | machine learning | quantum machine learning | quantum security. |
مقاله انگلیسی |
4 |
Quantum Computing for Applications in Data Fusion
محاسبات کوانتومی برای برنامه های کاربردی در ترکیب داده ها-2022 Quantum computing promises significant improvements of computation capabilities in various fields such as machine
learning and complex optimization problems. Rapid technological advancements suggest that adiabatic and gate base quantum
computing may see practical applications in the near future. In
this work, we adopt quantum computing paradigms to develop
solvers for two well–known combinatorial optimization problems
in information fusion and resource management: multi-target
data association (MTDA) and weapon target assignment (WTA).
These problems are NP-hard (non-)linear integer programming
optimization tasks which become computationally expensive for
large problem sizes. We derive the problem formulations adapted
for the use in quantum algorithms and present solvers based on
adiabatic quantum computing (AQC) and the Quantum Approximative Optimization Algorithm (QAOA). The feasibility of the models
is demonstrated by numerical simulation and first experiments on
quantum hardware.
Index Terms: adiabatic quantum computing | weapon-target assignment | data association | multi-target tracking | quantum gates | Ising model |
مقاله انگلیسی |
5 |
عوامل تعیین کننده باز بودن کسب و کار در فرآیندهای نوآوری
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 13 مفهوم نوآوری باز نه تنها به عنوان یک موضوع مطالعه در دانشگاهیان، بلکه به عنوان چارچوبی برای توسعه مدل های جدید مدیریت کسب و کار به اهمیت ویژه ای دست یافته است. این مقاله به بررسی عوامل تعیین کننده یکی از ابعاد نوآوری باز مرتبط با استفاده از دانش خارجی برای توسعه فرآیندهای نوآوری تجاری می پردازد. تجزیه و تحلیل بر اساس ریز داده های توسعه و نوآوری فناوری بررسی EDIT 2015 - 2016 انجام شده توسط آژانس آماری کلمبیا (DANE) انجام شده است. برای این منظور، معیاری که میزان باز بودن شرکت را در رابطه با استفاده از منابع اطلاعاتی خارجی برای توسعه فعالیتهای نوآورانه نشان میدهد، معرفی شده است. متغیرهای مرتبط با قابلیتهای فنآوری شرکت، موانع نوآوری و استراتژی مناسببودن به عنوان عوامل تعیینکننده در نظر گرفته میشوند.
کلیدواژه ها: نوآوری باز | منابع اطلاعاتی | تحقیق و توسعه | استراتژی های مناسب سازی |
مقاله ترجمه شده |
6 |
Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing
-2022 The increasing availability of quantum computers
motivates researching their potential capabilities in enhancing
the performance of data analysis algorithms. Similarly, as in
other research communities, also in remote sensing (RS), it is
not yet defined how its applications can benefit from the usage
of quantum computing (QC). This letter proposes a formulation
of the support vector regression (SVR) algorithm that can be
executed by D-Wave quantum computers. Specifically, the SVR
is mapped to a quadratic unconstrained binary optimization
(QUBO) problem that is solved with quantum annealing (QA).
The algorithm is tested on two different types of computing
environments offered by D-Wave: the advantage system, which
directly embeds the problem into the quantum processing unit
(QPU), and a hybrid solver that employs both classical and
QC resources. For the evaluation, we considered a biophysical
variable estimation problem with RS data. The experimental
results show that the proposed quantum SVR implementation
can achieve comparable or, in some cases, better results than the
classical implementation. This work is one of the first attempts to
provide insight into how QA could be exploited and integrated in
future RS workflows based on machine learning (ML) algorithms.
Index Terms: Quantum annealing (QA) | quantum computing (QC) | quantum machine learning (QML) | remote sensing (RS) | support vector regression (SVR). |
مقاله انگلیسی |
7 |
Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation
کامپایلرهای بهینه سازی مجدد قابل هدف گیری برای شتاب دهنده های کوانتومی از طریق یک نمایش میانی چند سطحی-2022 We present a multilevel quantum–classical intermediate representation (IR) that
enables an optimizing, retargetable compiler for available quantum languages.
Our work builds upon the multilevel intermediate representation (MLIR)
framework and leverages its unique progressive lowering capabilities to map
quantum languages to the low-level virtual machine (LLVM) machine-level IR.
We provide both quantum and classical optimizations via the MLIR pattern
rewriting subsystem and standard LLVM optimization passes, and demonstrate
the programmability, compilation, and execution of our approach via standard
benchmarks and test cases. In comparison to other standalone language and
compiler efforts available today, our work results in compile times that are
1,000 faster than standard Pythonic approaches, and 5–10 faster than
comparative standalone quantum language compilers. Our compiler provides
quantum resource optimizations via standard programming patterns that result
in a 10 reduction in entangling operations, a common source of program
noise. We see this work as a vehicle for rapid quantum compiler prototyping.
|
مقاله انگلیسی |
8 |
SpinQ Triangulum: A commercial three-qubit desktop quantum computer
SpinQ Triangulum: یک کامپیوتر کوانتومی رومیزی سه کیوبیتی تجاری-2022 SPINQ TRIANGULUM IS THE SEC- ond generation of the desktop quantum computers designed and manufactured by SpinQ Technology. SpinQ’s desk- SpinQ Triangulum
top quantum computer series, based on
a room-temperature nuclear magnetic
resonance (NMR) spectrometer, provides lightweight, cost-effective, and
maintenance-free quantum computing
platforms that aim to provide real-device
experience for quantum computing education for kindergarten through 12th
grade (K–12) and the college level. These
platforms also feature quantum control
design capabilities for studying quantum
control and quantum noise.
|
مقاله انگلیسی |
9 |
Non-functional requirements elicitation for edge computing
استخراج الزامات غیر عملکردی برای محاسبات لبه-2022 The proliferation of the Internet of Things (IoT) devices and advances in their computing
capabilities give an impetus to the Edge Computing (EC) paradigm that can facilitate localize computing and data storage. As a result, limitations like network connectivity issues,
data mobility constraints, and real-time processing delays, in Cloud computing can be addressed more efficiently. EC can create a lot of opportunities across the breadth of the
IT domains and cyber–physical systems. Several studies have been conducted describing EC
general requirements, challenges, and issues. However, considering the complexity involved
in the EC paradigm, non-functional requirements (NFRs) are equally important as functional
requirements, to be thoroughly investigated. This paper discusses NFRs, namely, performance,
reliability, scalability, and security that can assist in maturing the EC paradigm. To accomplish the objective, available case studies and the state-of-the-art related to non-functional
requirements, real-world issues, and challenges concerning EC are reviewed. Ultimately, the
paper anatomizes the aforementioned NFRs leveraging the six-part scenario form of sourcestimulus-artifact-environment-response-response measure to assert Quality of Service (QoS) in
EC.
Keywords: Edge Computing | Non functional requirements (quality attributes) | Quality of service |
مقاله انگلیسی |
10 |
Resource efficient AI: Exploring neural network pruning for task specialization
هوش مصنوعی کارآمد منابع: بررسی هرس شبکه عصبی برای تخصص در کار-2022 This paper explores the use of neural network pruning for transfer learning applications for more
resource-efficient inference. The goal is to focus and optimize a neural network on a smaller
specialized target task. With the advent of IoT, we have seen an immense increase in AI-based
applications on mobile and embedded devices, such as wearables and other smart appliances.
However, with the ever-increasing complexity and capabilities of machine learning algorithms,
this push to the edge has led to new challenges due to the constraints imposed by the limited
availability of resources on these devices. Some form of compression is needed to allow for stateof-the-art convolutional neural networks to run on edge devices. In this work, we adapt existing
neural network pruning methods to allow them to specialize networks to only focus on a subset
of what they were originally trained for. This is a transfer learning use-case where we optimize
large pre-trained networks. This differs from standard optimization techniques by allowing the
network to forget certain concepts and allow the network’s footprint to be even smaller. We
compare different pruning criteria, including one from the field of Explainable AI (XAI), to
determine which technique allows for the smallest possible network while maintaining high
performance on the target task. Our results show the benefits of using network specialization
when executing neural networks on embedded devices both with and without GPU acceleration.
keywords: فشرده سازی شبکه عصبی | یادگیری ماشین | هوش مصنوعی قابل توضیح | هرس شبکه عصبی | استنتاج لبه | Neural network compression | Machine learning | Explainable AI | Neural network pruning | Edge inference |
مقاله انگلیسی |