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ردیف | عنوان | نوع |
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41 |
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 |
42 |
A computer vision-based method for bridge model updating using displacement influence lines
یک روش مبتنی بر بینایی کامپیوتری برای بهروزرسانی مدل پل با استفاده از خطوط موثر جابجایی-2022 This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s
tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the
bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the
displacement influence lines of the bridge at different target positions, which is then later used to perform
model updating of the finite element models of the monitored structural system.
The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the coauthors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads. The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements. The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring. keywords: Computer vision | Displacement influence line | Vehicle weigh-in-motion | Structural identification | Finite element method model | Model updating | Modal analysis | Bridge systems |
مقاله انگلیسی |
43 |
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 |
44 |
Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022 Computer vision systems perform based on their design and parameter setting. In computer vision systems
that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of
the systems in terms of both results quality and computational costs. Appropriate setting of the weights for
the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision
system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or
no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the
grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the
system and subsystem to small changes in the distribution of data in a color space of the processed images.
We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the
advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show
that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies.
The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer
vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion |
مقاله انگلیسی |
45 |
Entropic Proofs of Singleton Bounds for Quantum Error-Correcting Codes
اثبات های آنتروپیک کرانه های سینگلتون برای کدهای تصحیح خطای کوانتومی-2022 We show that a relatively simple reasoning using
von Neumann entropy inequalities yields a robust proof of the
quantum Singleton bound for quantum error-correcting codes
(QECC). For entanglement-assisted quantum error-correcting
codes (EAQECC) and catalytic codes (CQECC), a type of
generalized quantum Singleton bound [Brun et al., IEEE Trans.
Inf. Theory 60(6):3073–3089 (2014)] was believed to hold for
many years until recently one of us found a counterexample
[MG, Phys. Rev. A 103, 020601 (2021)]. Here, we rectify this state
of affairs by proving the correct generalized quantum Singleton
bound, extending the above-mentioned proof method for QECC;
we also prove information-theoretically tight bounds on the
entanglement-communication tradeoff for EAQECC. All of the
bounds relate block length n and code length k for given
minimum distance d and we show that they are robust, in the
sense that they hold with small perturbations for codes which
only correct most of the erasure errors of less than d letters.
In contrast to the classical case, the bounds take on qualitatively
different forms depending on whether the minimum distance
is smaller or larger than half the block length. We also provide a propagation rule: any pure QECC yields an EAQECC
with the same distance and dimension, but of shorter block
length.
Index Terms: Quantum codes | quantum entanglement | singleton bound. |
مقاله انگلیسی |
46 |
Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیشبینی تعامل رسانههای اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022 In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta-
grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’
Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to
social media engagement. Results demonstrate that food images that are more confidently evaluated by Google
Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-
up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier
to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and
food industry trends, the more typical a food appears, the more social media engagement it receives. Using
Google Vision AI to identify what product offerings receive engagement presents an accessible method for
marketers to understand their industry and inform their social media marketing strategies. keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI |
مقاله انگلیسی |
47 |
EP-PQM: Efficient Parametric Probabilistic Quantum Memory With Fewer Qubits and Gates
EP-PQM: حافظه کوانتومی احتمالی پارامتریک کارآمد با کیوبیت ها و گیت های کمتر-2022 Machine learning (ML) classification tasks can be carried out on a quantum computer (QC)
using probabilistic quantum memory (PQM) and its extension, parametric PQM (P-PQM), by calculating
the Hamming distance between an input pattern and a database of r patterns containing z features with
a distinct attributes. For PQM and P-PQM to correctly compute the Hamming distance, the feature must
be encoded using one-hot encoding, which is memory intensive for multiattribute datasets with a > 2. We
can represent multiattribute data more compactly by replacing one-hot encoding with label encoding; both
encodings yield the same Hamming distance. Implementing this replacement on a classical computer is
trivial. However, replacing these encoding schemes on a QC is not straightforward because PQM and P-PQM
operate at the bit level, rather than at the feature level (a feature is represented by a binary string of 0’s and
1’s). We present an enhanced P-PQM, called efficient P-PQM (EP-PQM), that allows label encoding of data
stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures.
We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device. Our
complexity analysis shows that the EP-PQM approach requires O(z log2(a)) qubits as opposed to O(za)
qubits for P-PQM. EP-PQM also requires fewer gates, reducing gate count from O(rza) to O(rz log2(a)).
For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to
77% fewer qubits than P-PQM for datasets with a > 2. EP-PQM reduces circuit depth in the range of 60% to
96%, depending on the dataset. The depth decreases further with a decomposed circuit, ranging between 94%
and 99%. EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM
implementations on NISQ devices. Furthermore, reducing the number of gates speeds up the classification
and reduces the noise associated with deep quantum circuits. Thus, EP-PQM brings us closer to scalable ML
on an NISQ device.
INDEX TERMS: Efficient encoding | label encoding | quantum memory. |
مقاله انگلیسی |
48 |
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022 Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture,
engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However,
research on KG updates in the industry is scarce, with most current research focusing on text-based KG
updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the
potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex-
plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to
propose a novel framework called Image2Triplets that can extract explicit visual relationships from images
to update the construction activity KG. Comprehensive experiments on the images of architectural dec-
oration processes have been performed to validate the proposed framework. The results and insights will
contribute new knowledge and evidence to human-object interaction detection, KG update and construc-
tion informatics from the theoretical perspective.
© 2022 Elsevier B.V. All rights reserved. keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph |
مقاله انگلیسی |
49 |
Epsilon-Nets, Unitary Designs, and Random Quantum Circuits
شبکه های اپسیلون، طرح های واحد و مدارهای کوانتومی تصادفی-2022 Epsilon-nets and approximate unitary t-designs are
natural notions that capture properties of unitary operations
relevant for numerous applications in quantum information
and quantum computing. In this work we study quantitative
connections between these two notions. Specifically, we prove
that, for d dimensional Hilbert space, unitaries constituting
δ-approximate t-expanders form -nets for t d5/2 and δ
3d/2 d2. We also show that for arbitrary t, -nets can be used
to construct δ-approximate unitary t-designs for δ t, where
the notion of approximation is based on the diamond norm.
Finally, we prove that the degree of an exact unitary t design
necessary to obtain an -net must grow at least as fast as 1 (for
fixed dimension) and not slower than d2 (for fixed ). This shows
near optimality of our result connecting t-designs and nets.
We apply our findings in the context of quantum computing.
First, we show that that approximate t-designs can be generated
by shallow random circuits formed from a set of universal twoqudit gates in the parallel and sequential local architectures
considered in (Brandão et al., 2016). Importantly, our gate sets
need not to be symmetric (i.e., contains gates together with
their inverses) or consist of gates with algebraic entries. Second,
we consider compilation of quantum gates and prove a nonconstructive Solovay-Kitaev theorem for general universal gate
sets. Our main technical contribution is a new construction of
efficient polynomial approximations to the Dirac delta in the
space of quantum channels, which can be of independent interest.]
Index Terms: Unitary designs, epsilon nets | random quantum circuits | compilation of quantum gates | unitary channels. |
مقاله انگلیسی |
50 |
A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022 Deep learning has been widely applied in various fields such as computer vision, natural language pro-
cessing, and data mining. Although deep learning has achieved significant success in solving complex
problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result-
ing in models that fail to perform their tasks properly, which limits the application of deep learning
in security-critical areas. In this paper, we first review some of the classical and latest representative
adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl-
edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze
the subject development in this field based on the information of 5923 articles from Scopus. In the
end, possible research directions for the development about adversarial attacks are proposed based on
the trends deduced by keywords detection analysis. All the data used for visualization are available at:
https://github.com/NanyunLengmu/Adversarial- Attack- Visualization . keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis |
مقاله انگلیسی |