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1 |
Enabling Pulse-Level Programming, Compilation, and Execution in XACC
فعال کردن برنامه نویسی، کامپایل و اجرا در سطح پالس در XACC-2022 Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital
quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately
lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital
circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming
methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove
critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal
pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software
framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and
extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom
backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with
a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.
Index Terms: Quantum computing | quantum programming models | quantum control | quantum simulation |
مقاله انگلیسی |
2 |
Photonic Quantum Computers Enlighten the World: A review of their development, types, and applications
کامپیوترهای کوانتومی فوتونیک جهان را روشن می کنند: مروری بر توسعه، انواع و کاربردهای آنها-2022 IT HAS BEEN DEMONSTR ATED
that the photonic quantum computer
is significantly faster than conventional
supercomputers and that the practical
quantum computer is one of the most
promising ways to solve real-life problems. In this article, the development
of photonic quantum computers and
their potential applications are summarized, and three types of photonic quantum computing machines are detailed,
including photonic quantum machines,
coherent Ising machines (CIMs), and
programmable photonic quantum computers. The photonic quantum industry,
together with some start-up companies
in associated application fields, are profiled. Compared with superconducting and ion traps, photonic quantum
computing has its own advantages and
disadvantages. It will be seen whether
photonic quantum computer companies
can capture a share of the future quantum computing market. The major challenges lie in the scalability of photonic
systems and their adaptation and integration with silicon (Si) systems.
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مقاله انگلیسی |
3 |
Tunneling Current Through a Double Quantum Dots System
جریان تونل زنی از طریق سیستم دو نقطه کوانتومی-2022 Electrostatically confined quantum dots in semiconductors hold the promise to achieve high
scalability and reliability levels for practical implementation of solid-state qubits where the electrochemical
potentials of each quantum dot can be independently controlled by the gate voltages.In this paper, the
current and charge stability diagram of two-well potentials arising from electrostatically defined double
quantum dot (DQD) are analytically realized. We propose to apply the Generalized Hubbard model to find
the Hamiltonian of the system. The proposed analysis takes the tunnel coupling between the dots, Coulomb
interaction, and Zeeman energy arising from an external magnetic field into account. Using quantum master
equations to predict the probability of the final states in a DQD system, we study the tunneling current
through two quantum dots coupled in series with two conducting leads, and therefore, the charge stability
diagram is theoretically investigated. The impact of the tunnel coupling and Zeeman energy on the charge
stability diagram is deeply discussed. The validity of the presented analysis is confirmed by experimental
data as well as the classical capacitance model.
INDEX TERMS: Double quantum dot | hubbard model | zeeman energy | charge stability diagram | master equation. |
مقاله انگلیسی |
4 |
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 |
5 |
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022 Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person
contacts, enabling early-detection of severe cases, and remotely assessing patients’ status.
Internet of Things (IoT) technologies have been used for monitoring patients’ health with
wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning
scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results
in detecting the health deterioration of COVID-19 patients. Although the literature presents
several approaches for remote monitoring, none of these studies proposes a customized,
complete, and integrated architecture that uses an effective early-detection mechanism for
COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore,
this article’s objective is to present a comprehensive IoT-based conceptual architecture that
addresses the key requirements of scalability, interoperability, network dynamics, context
discovery, reliability, and privacy in the context of remote health monitoring of COVID-19
patients in hospitals and at home. Since remote monitoring of patients at home (essential
during a pandemic) can engender trust issues regarding secure and ethical data collection,
a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a
configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT |
مقاله انگلیسی |
6 |
A flexible Compilation-as-a-Service and Remote-Programming-as-a-Service platform for IoT devices
یک پلت فرم انعطاف پذیر مجموعه به عنوان سرویس و برنامه نویسی راه دور به عنوان سرویس برای دستگاه های اینترنت اشیا-2022 The Internet-of-Things (IoT) presents itself as an emerging technology, which is able to interconnect a massive number of heterogeneous smart objects. Several complex data-driven applications, such as smart cities applications, home automation, health monitoring, etc., have been
realized through the existence of these ubiquitous networks of smart objects. The ability to remotely update the devices forming an IoT network is of paramount importance, as it enables
adding new functionality in their firmware, either for resolving software bugs and security vulnerabilities or for application re-purposing, without the need to physically access them. In this
work, we present a flexible Compilation-as-a-Service and Remote-Programming-as-a-Service
platform that jointly offers cloud-based compilation and Firmware-Over-The-Air (FOTA) update
functionalities for deployed IoT devices, in a reliable and secure manner. Our system is capable
of easily supporting various embedded operating systems and heterogeneous hardware platforms.
We describe the system architecture and elaborate on the implementation details of all system
components. In addition, we perform an extensive performance evaluation of a Proof-of-Concept
(PoC) deployment of our system and discuss results in terms of system response, scalability and
resource utilization.
keywords: Internet-of-Things | Cloud computing | Platform-as-a-Service | Cloud compilation | Over-the-air programming |
مقاله انگلیسی |
7 |
A survey of blockchain-based IoT eHealthcare: Applications, research issues, and challenges
بررسی مراقبت های بهداشتی الکترونیک اینترنت اشیاء مبتنی بر بلاک چین: برنامه های کاربردی، مسائل تحقیقاتی و چالش ها-2022 Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance,
and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to
expand their scalability and maintain consistency on a decentralized platform. As a result, many
researchers have developed BC-enabled IoT eHealth systems and explored the application of
BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey
on the emerging applications of BC technology in healthcare. We summarize applications,
research issues, security threats, research challenges, opportunities, and the future scope of BC
technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy
and storage of current and future medical records. Furthermore, we analyze the state-of-the-art
BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers
to overcome the limitations of the existing articles.
Keywords: Blockchain | IoT | Healthcare | EHR challenge | Medical area |
مقاله انگلیسی |
8 |
Attention-based model and deep reinforcement learning for distribution of event processing tasks
مدل مبتنی بر توجه و یادگیری تقویتی عمیق برای توزیع وظایف پردازش رویداد-2022 Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT).
Recent approaches in this area are based on representational state transfer (REST) principles,
which allow event processing tasks to be placed at any device that follows the same principles.
However, the tasks should be properly distributed among edge devices to ensure fair resources
utilization and guarantee seamless execution. This article investigates the use of deep learning
to fairly distribute the tasks. An attention-based neural network model is proposed to generate
efficient load balancing solutions under different scenarios. The proposed model is based on
the Transformer and Pointer Network architectures, and is trained by an advantage actorcritic reinforcement learning algorithm. The model is designed to scale to the number of
event processing tasks and the number of edge devices, with no need for hyperparameters
re-tuning or even retraining. Extensive experimental results show that the proposed model
outperforms conventional heuristics in many key performance indicators. The generic design
and the obtained results show that the proposed model can potentially be applied to several
other load balancing problem variations, which makes the proposal an attractive option to be
used in real-world scenarios due to its scalability and efficiency.
keywords: Web of Things (WoT) | Representational state transfer (REST) | application programming interface (APIs) | Edge computing | Load balancing | Resource placement | Deep reinforcement leaning | Transformer model | Pointer networks | Actor critic |
مقاله انگلیسی |
9 |
Efficient biometric-based identity management on the Blockchain for smart industrial applications
مدیریت هویت مبتنی بر بیومتریک کارآمد در Blockchain برای کاربردهای صنعتی هوشمند-2021 In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.© 2020 Elsevier B.V. All rights reserved. Keywords: Identity management | Smart industry | Blockchain | Non-transferability | Biometrics | DLRep | Multi-show unlinkability | Selective disclosure | Accumulators |
مقاله انگلیسی |
10 |
Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance
طبقه بندی خودکار جانوران در عکس های بستر دریا: تأثیر اندازه مجموعه داده های آموزش و اعتبار سنجی ، با ملاحظاتی برای عدم تعادل کلاس-2021 Machine learning is rapidly developing as a tool for gathering data from imagery and may be useful in identifying (classifying) visible specimens in large numbers of seabed photographs. Application of an automated classifi- cation workflow requires manually identified specimens to be supplied for training and validating the model. These training and validation datasets are generally generated by partitioning the available manual identified specimens; typical ratios of training to validation dataset sizes are 75:25 or 80:20. However, this approach does not facilitate the desired scalability, which would require models to successfully classify specimens in hundreds of thousands to millions of images after training on a relatively small subset of manually identified specimens. A second problem is related to the ‘class imbalance’, where natural community structure means that fewer spec- imens of rare morphotypes are available for model training. We investigated the impact of independent variation of the training and validation dataset sizes on the performance of a convolutional neural network classifier on benthic invertebrates visible in a very large set of seabed photographs captured by an autonomous underwater vehicle at the Porcupine Abyssal Plain Sustained Observatory. We tested the impact of increasing training dataset size on specimen classification in a single validation dataset, and then tested the impact of increasing validation set size, evaluating ecological metrics in addition to computer vision metrics. Computer vision metrics (recall, precision, F1-score) indicated that classification improved with increasing training dataset size. In terms of ecological metrics, the number of morphotypes recorded increased, while diversity decreased with increasing training dataset size. Variation and bias in diversity metrics decreased with increasing training dataset size. Multivariate dispersion in apparent community composition was reduced, and bias from expert-derived data declined with increasing training dataset size. In contrast, classification success and resulting ecological metrics did not differ significantly with varying validation dataset sizes. Thus, the selection of an appropriate training dataset size is key to ensuring robust automated classifications of benthic invertebrates in seabed photographs, in terms of ecological results, and validation may be conducted on a comparatively small dataset with confidence that similar results will be obtained in a larger production dataset. In addition, our results suggest that automated classification of less common morphotypes may be feasible, providing that the overall training dataset size is sufficiently large. Thus, tactics for reducing class imbalance in the training dataset may produce improvements in the resulting ecological metrics. Keywords: Computer vision | Deep learning | Benthic ecology | Image annotation | Marine photography | Artificial intelligence | Convolutional neural networks | Sample size |
مقاله انگلیسی |