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ردیف | عنوان | نوع |
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1 |
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 |
2 |
Efficient Construction of a Control Modular Adder on a Carry-Lookahead Adder Using Relative-Phase Toffoli Gates
ساخت کارآمد یک جمع کننده ماژولار کنترلی بر روی جمع کننده Carry-Lookahead با استفاده از گیت های توفولی فاز نسبی-2022 Control modular addition is a core arithmetic function, and we must consider the computational cost for actual quantum computers to realize efficient implementation. To achieve a low computational
cost in a control modular adder, we focus on minimizing KQ (where K is the number of logical qubits
required by the algorithm, and Q is the elementary gate step), defined by the product of the number of
qubits and the depth of the circuit. In this article, we construct an efficient control modular adder with small
KQ by using relative-phase Toffoli gates in two major types of quantum computers: fault-tolerant quantum
computers (FTQ) on the logical layer and noisy intermediate-scale quantum computers (NISQ). We give
a more efficient construction compared with Van Meter and Itoh’s, based on a carry-lookahead adder. In
FTQ, T gates incur heavy cost due to distillation, which fabricates ancilla for running T gates with high
accuracy but consumes a lot of especially prepared ancilla qubits and a lot of time. Thus, we must reduce the
number of T gates. We propose a new control modular adder that uses only 20% of the number of T gates
of the original. Moreover, when we take distillation into consideration, we find that we minimize KQT (the
product of the number of qubits and T-depth) by running (n/√log n) T gates simultaneously. In NISQ,
cnot gates are the major error source. We propose a new control modular adder that uses only 35% of the
number of cnotgates of the original. Moreover, we show that the KQCX (the product of the number of qubits
and cnot-depth) of our circuit is 38% of the original. Thus, we realize an efficient control modular adder,
improving prospects for the efficient execution of arithmetic in quantum computers.
INDEX TERMS: Carry-lookahead adder | control modular adder | fault-tolerant quantum computers (FTQ) | noisy intermediate-scale quantum computers (NISQ) | Shor’s algorithm. |
مقاله انگلیسی |
3 |
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 |
4 |
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 |
5 |
Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022 Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry.
Disintegration testing is often the first step of commercialization and large-scale production of these dosage
forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia
(USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor-
mation is subjective, variable, and prone to human error due to manual or physical data collection methods via
the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been
made to automate the analysis of the disintegration process using digital lens and other imaging technologies.
This would provide a non-invasive method to quantitatively determine disintegration time through computer
algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces
through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to
be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and
distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or
other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze
and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not
only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of
disintegration rates and mechanisms in addition to duration. keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks |
مقاله انگلیسی |
6 |
Measurement Crosstalk Errors in Cloud-Based Quantum Computing
خطاهای متقابل اندازه گیری در محاسبات کوانتومی مبتنی بر ابر-2022 Quantum technologies available currently contain noise in general, often dubbed
noisy intermediate-scale quantum systems. We here present the verification of
noise in measurement readout errors in cloud-based quantum computing services,
IBMQ and Rigetti, by directly performing quantum detector tomography, and show
that there exist measurement crosstalk errors. We provide the characterization and
the quantification of noise in a quantum measurement of multiple qubits. We
remark that entanglement is found as a source of crosstalk errors in a
measurement of three qubits.
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مقاله انگلیسی |
7 |
Mobile Control Plane Design for Quantum Satellite Backbones
طراحی هواپیمای کنترل سیار برای ستون فقرات ماهواره ای کوانتومی-2022 The interconnection of quantum computers
through the so-called Quantum Internet is a very
promising approach.
The most critical issues concern the physical
layer, considering that the creation of entanglement over long distances is still problematic.
Given the difficulty that usually arises from fiber
optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs)
allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed
over low Earth orbit could make it possible to
overcome the constraints of terrestrial optical
networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad
hoc quantum satellite backbone design based on
the software defined networking paradigm with a
control plane directly integrated into the constellation itself. Our aim is to outline some guidelines
by comparing several options. Specifically, the
focus is to analyze different architectural solutions
making some considerations on their feasibility,
possible benefits, and costs. Finally, we performed
some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation
itself is the most appropriate solution.
keywords: |
مقاله انگلیسی |
8 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
9 |
VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data
تجسم ها به عنوان بازنمایی های میانی (VLAIR): رویکردی برای به کارگیری بینایی کامپیوتری مبتنی بر یادگیری عمیق برای داده های غیر مبتنی بر تصویر-2022 Deep learning algorithms increasingly support automated systems in areas such as human activity
recognition and purchase recommendation. We identify a current trend in which data is transformed
first into abstract visualizations and then processed by a computer vision deep learning pipeline. We
call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental
to support accurate recognition in a number of fields while also enhancing humans’ ability to
interpret deep learning models for debugging purposes or for personal use. In this paper we describe
the potential advantages of this approach and explore various visualization mappings and deep
learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity
recognition in an apartment) and show that VLAIR attains classification accuracy above classical
machine learning algorithms and several other non-image-based deep learning algorithms with several
data representations.
keywords: تجسم اطلاعات | شبکه های عصبی کانولوشنال | تشخیص فعالیت های انسانی | خانه های هوشمند | بازنمایی داده ها | نمایندگی های میانی | تفسیر پذیری | یادگیری ماشین | یادگیری عمیق | Information visualization | Convolutional neural networks | Human activity recognition | Smart homes | Data representation | Intermediate representations | Interpretability | Machine learning | Deep learning |
مقاله انگلیسی |
10 |
On the Realistic Worst-Case Analysis of Quantum Arithmetic Circuits
در مورد تحلیل واقعی بدترین حالت مدارهای محاسباتی کوانتومی-2022 We provide evidence that commonly held intuitions when designing quantum circuits can be
misleading. In particular, we show that 1) reducing the T-count can increase the total depth; 2) it may be
beneficial to trade controlled NOTs for measurements in noisy intermediate-scale quantum (NISQ) circuits;
2) measurement-based uncomputation of relative phase Toffoli ancillae can make up to 30% of a circuit’s
depth; and 4) area and volume cost metrics can misreport the resource analysis. Our findings assume that
qubits are and will remain a very scarce resource. The results are applicable for both NISQ and quantum errorcorrected protected circuits. Our method uses multiple ways of decomposing Toffoli gates into Clifford+T
gates. We illustrate our method on addition and multiplication circuits using ripple-carry. As a byproduct
result, we show systematically that for a practically significant range of circuit widths, ripple-carry addition
circuits are more resource-efficient than the carry-lookahead addition ones. The methods and circuits were
implemented in the open-source QUANTIFY software.
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مقاله انگلیسی |