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ردیف | عنوان | نوع |
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1 |
Discriminating Quantum States in the Presence of a Deutschian CTC: A Simulation Analysis
حالت های کوانتومی متمایز در حضور CTC Deutschian: یک تحلیل شبیه سازی-2022 In an article published in 2009, Brun et al. proved that in the presence of a “Deutschian”
closed timelike curve, one can map K distinct nonorthogonal states (hereafter, input set) to the standard
orthonormal basis of a K-dimensional state space. To implement this result, the authors proposed a quantum
circuit that includes, among SWAP gates, a fixed set of controlled operators (boxes) and an algorithm for
determining the unitary transformations carried out by such boxes. To our knowledge, what is still missing
to complete the picture is an analysis evaluating the performance of the aforementioned circuit from an
engineering perspective. The objective of this article is, therefore, to address this gap through an in-depth
simulation analysis, which exploits the approach proposed by Brun et al. in 2017. This approach relies on
multiple copies of an input state, multiple iterations of the circuit until a fixed point is (almost) reached. The
performance analysis led us to a number of findings. First, the number of iterations is significantly high even
if the number of states to be discriminated against is small, such as 2 or 3. Second, we envision that such
a number may be shortened as there is plenty of room to improve the unitary transformation acting in the
aforementioned controlled boxes. Third, we also revealed a relationship between the number of iterations
required to get close to the fixed point and the Chernoff limit of the input set used: the higher the Chernoff
bound, the smaller the number of iterations. A comparison, although partial, with another quantum circuit
discriminating the nonorthogonal states, proposed by Nareddula et al. in 2018, is carried out and differences
are highlighted.
INDEX TERMS: Benchmarking and performance characterization | classical simulation of quantum systems. |
مقاله انگلیسی |
2 |
Efficient Hardware Implementation of Finite Field Arithmetic AB + C for Binary Ring-LWE Based Post-Quantum Cryptography
اجرای سخت افزار کارآمد محاسبات میدان محدود AB + C برای رمزنگاری پس کوانتومی مبتنی بر حلقه باینری-LWE-2022 Post-quantum cryptography (PQC) has gained significant attention from the community
recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from
the well-developed quantum computers. The finite field arithmetic AB þ C, where A and C are integer polynomials and B is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)-
based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper,
we propose a novel hardware implementation of the finite field arithmetic AB þ C through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware
architecture is then presented with detailed description; (iii) a thorough implementation has also been given
along with the comparison. Overall, (i) the proposed basic structure (u ¼ 1) outperforms the existing designs,
e.g., it involves 55.9% less area-delay product (ADP) than [13] for n ¼ 512; (ii) the proposed design also offers
very efficient performance in time-complexity and can be used in many future applications.
INDEX TERMS: Binary ring-learning-with-errors | finite field arithmetic | FPGA platform | hardware design | post-quantum cryptography |
مقاله انگلیسی |
3 |
Head tremor in cervical dystonia: Quantifying severity with computer vision
لرزش سر در دیستونی دهانه رحم: کمی کردن شدت با دید کامپیوتری-2022 Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective
observation. Technological developments offer alternatives for measuring HT severity that are objective and
amenable to automation.
Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-
based software system) to quantify oscillatory and directional aspects of HT from video recordings during a
clinical examination and to test its convergent validity with clinical rating scales.
Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video
recordings from an examination segment in which participants were instructed to let their head drift to its most
comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used
Spearman’s correlation to measure the agreement between CMOR and clinical ratings.
Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis
was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants.
One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t =
12.89, p < 0.0001) HT axes. CMOR’s HT severity measure positively correlated with the HT item on the Toronto
Western Spasmodic Torticollis Rating Scale-2 (Spearman’s rho = 0.54, p < 0.001).
Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional
video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity
ratings. keywords: لرزش سر | ویدیو | بینایی کامپیوتر | درجه بندی شدت | TWSTRS | Head tremor | Video | Computer vision | Severity rating | TWSTRS |
مقاله انگلیسی |
4 |
High-Performance Reservoir Computing With Fluctuations in Linear Networks
محاسبات مخزن با کارایی بالا با نوسانات در شبکه های خطی-2022 Reservoir computing has emerged as a powerful
machine learning paradigm for harvesting nontrivial information
processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become
nonlinear functions of the input history. We show that encoding
the input to quantum or classical fluctuations of a network of
interacting harmonic oscillators can lead to a high performance
comparable to that of a standard echo state network in several
nonlinear benchmark tasks. This equivalence in performance
holds even with a linear Hamiltonian and a readout linear in the
system observables. Furthermore, we find that the performance of
the network of harmonic oscillators in nonlinear tasks is robust to
errors both in input and reservoir observables caused by external
noise. For any reservoir computing system with a linear readout,
the magnitude of trained weights can either amplify or suppress
noise added to reservoir observables. We use this general result to
explain why the oscillators are robust to noise and why having
precise control over reservoir memory is important for noise
robustness in general. Our results pave the way toward reservoir
computing harnessing fluctuations in disordered linear systems.
Index Terms: Dynamical systems | machine learning | quantum mechanics | recurrent neural networks | reservoir computing | supervised learning. |
مقاله انگلیسی |
5 |
Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling
تکنیکهای بهینهسازی ترکیبی کلاسیک-کوانتومی برای حل مسائل برنامهنویسی عدد صحیح مختلط در زمانبندی تولید-2022 Quantum computing (QC) holds great promise to open up a new era of computing and has been
receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing
the current quantum computers to complement classical techniques for solving real-world problems is of
utmost importance. In this article, we develop QC-based solution strategies that exploit quantum annealing
and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems.
The applications of the proposed algorithms are illustrated through two case studies in production scheduling.
First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multipurpose batch scheduling problem with a fractional
objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer
linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance
comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution
techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical
solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established
deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf
optimization solvers.
INDEX TERMS: Hybrid techniques | optimization | quantum annealing | quantum computing (QC) | scheduling. |
مقاله انگلیسی |
6 |
PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022 In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity.
However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop-
ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a
solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous,
real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed
with all the steps necessary for the system to operate, from hardware specifications and data collection to su-
pervisory information obtained by deep learning and statistical analysis. The overall system was tested and
validated in an operational environment in a material recovery facility.
PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2%
precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%.
Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively,
giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed
per batch, the detection results were used to estimate purity and its confidence level. The estimation error was
calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon-
strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can
stream. keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision |
مقاله انگلیسی |
7 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
8 |
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
روش ترکیبی کوانتومی-کلاسیک برای تشخیص تقلب با انتخاب ویژگی کوانتومی-2022 This paper presents a first end-to-end application of a Quantum Support Vector Machine
(QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer
Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment
data, a thorough comparison is performed to assess the complementary impact brought in by the current
state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new
method to search for best features is explored using the Quantum Support Vector Machine’s feature map
characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall,
and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical
machine learning algorithms (Random Forest, XGBoost) and quantum-based machine learning algorithms
using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model
that combines classical and quantum algorithms to better improve the fraud prevention decision. We found,
as expected, that the results highly depend on feature selections and algorithms that are used to select them.
The QSVM provides a complementary exploration of the feature space which led to an improved accuracy
of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current
state of Quantum Hardware.
INDEX TERMS: Fraud Detection | Quantum | Feature Selection | QSVM | Quantum Kernel Alignment |
مقاله انگلیسی |
9 |
New Advanced Computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer
معماری محاسباتی پیشرفته جدید برای طراحی و تحلیل رمزنگاری توسط D-Wave Quantum Annealer-2022 Universal quantum computers are far from achieving practical applications. The D-Wave quantum computer
is initially designed for combinatorial optimizations. Therefore, exploring the potential applications of the D-Wave
device in the field of cryptography is of great importance. First, although we optimize the general quantum Hamiltonian
on the basis of the structure of the multiplication table (factor up to 1 005 973), this study attempts to explore the
simplification of Hamiltonian derived from the binary structure of the integers to be factored. A simple factorization
on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the
D-Wave device. Second, by using the quantum computing cryptography based on the D-Wave 2000Q system, this
research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired
Simulated Annealing (QISA) framework. Good functions and a high-performance platform are introduced, and
additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found. Further
comparison between QISA and Quantum Annealing (QA) on six-variable bent functions not only shows the potential
speedup of QA, but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a
practical cryptography design.
Keywords: Quantum Annealing (QA) | factorization | Boolean functions | brain-inspired cognition |
مقاله انگلیسی |
10 |
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 |