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Characterization, synthesis, and optimization of quantum circuits over multiple-control Z-rotation gates: A systematic study

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Physical Review A 2023年第2期108卷 022603-022603页

作者： Shihao Zhang Junda Wu Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China

We conduct a systematic study of quantum circuits composed of multiple-control Z-rotation (mczr) gates as primitives, since they are widely used components in quantum algorithms and also have attracted much experimental interest in recent years. We establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the mczr gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any mczr circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical investigation, the practical performance of our circuit synthesis and optimization techniques is further evaluated numerically on two typical examples in quantum computing, including diagonal Hermitian operators and quantum approximate optimization algorithm circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40% and 15.55% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.

关键词： Iterative methods

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Foundry-fabricated photonic integrated circuit for flex-grid entanglement distribution

Foundry-fabricated photonic integrated circuit for flex-grid...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Miloshevsky, Alexander Lu, Hsuan-Hao Cohen, Lucas M. Myilswamy, Karthik V. Fatema, Saleha Alshowkan, Muneer Weiner, Andrew M. Lukens, Joseph M. Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781957171395

We demonstrate a silicon photonic integrated circuit fabricated through the CMOS manufacturing process, which features a bidirectionally pumped microring to achieve over 116 high-fidelity polarization entangled channels covering the entire optical C+L-band for flex-grid entanglement distribution. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Silicon photonics

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CiDAR-Former: Cosine-Weighting Deep Abundance Reconstruction Transformer for Fast Unsupervised Hyperspectral Anomaly Detection 13

CiDAR-Former: Cosine-Weighting Deep Abundance Reconstruction...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Young, Si-Sheng Lin, Chia-Hsiang Lin, Jhao-Ting National Cheng Kung University Institute of Computer and Communication Engineering Department of Electrical Engineering Tainan Taiwan National Cheng Kung University Miin Wu School of Computing Tainan Taiwan

ISBN: (纸本)9798350395570

Hyperspectral anomaly detection (HAD) aims to classify those targets with distinctive spectral characteristics compared to the background. Although hyperspectral images (HSIs) have high spectral resolution for material identification, they are usually accompanied by over correlated and redundant information. Moreover, the mixed pixel phenomenon caused by the satellite hardware limitation weakens the distinguishability between abnormal pixels and their surrounding background pixels, making HAD a challenging inverse problem. This paper develops an unsupervised cosine-weighting deep abundance-reconstruction transformer (CiDAR-Former) to address the aforementioned issues. CiDAR-Former is the first work employing deep learning, together with a trickily designed cosine-similarity loss for suppressing anomaly effects, to unsupervisedly and effectively reconstruct clean abundances for the subsequent HAD. Real data experiments will demonstrate the superiority of the proposed CiDAR-Former both qualitatively and quantitatively. © 2023 IEEE.

关键词： Inverse problems

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Spectral compression of biphotons using time-varying cavities for quantum networking

Spectral compression of biphotons using time-varying cavitie...

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CLEO: Science and Innovations, S and I 2022

作者： Myilswamy, Karthik V. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

ISBN: (数字)9781957171050

ISBN: (纸本)9781557528209

We propose and analyze the use of linear, time-variant cavities for spectral compression of broadband frequency correlated photon pairs, with potential applications in quantum networking. Our time-varying cavity relies on rapid electro-optic switching of input coupling to the cavity. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Frequency combs Phase modulation Photons quantum information processing quantum memories quantum networks

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Phishing Detection with Browser Extension Based on Machine Learning 18

Phishing Detection with Browser Extension Based on Machine L...

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18th Asia Joint Conference on Information Security, AsiaJCIS 2023

作者： Wu, Che-Yu Kuo, Cheng-Chung Yang, Chu-Sing National Cheng Kung University Institute of Computer and Communication Engineering Department of Electrical Engineering Tainan Taiwan National Cheng Kung University Miin Wu School of Computing Tainan Taiwan

ISBN: (纸本)9798350341638

Social engineering has become an emergent field with the growth of network attacks. Phishing, is a popular social engineering attack types, has also become an important research topic in the network security field. In this study, we implemented a browser extension for detecting phishing websites based on a machine learning mechanism. The proposed front-end browser extension can detect phishing websites in real time and hence, prevent users from inputting their personal information to these sites. The experimental results show that the proposed detection mechanism can reach a high accuracy up to 97.5% within 4.5 s with 150 users on average. © 2023 IEEE.

关键词： Machine learning

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Observing quantum Measurement Collapse as a Learnability Phase Transition

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Physical Review X 2024年第4期14卷 041012页

作者： Utkarsh Agrawal Javier Lopez-Piqueres Romain Vasseur Sarang Gopalakrishnan Andrew C. Potter Santa Barbara California 93106 USA Department of Physics Department of Electrical and Computer Engineering Department of Physics and Astronomy and Quantum Matter Institute

During a quantum measurement, superpositions of states with different observable properties probabilistically collapse into one with a sharp value of the measured observable. In macroscopic quantum systems, this collapse arises via a continuous measurement-induced phase transition (MIPT) at a critical value of the strength of interaction with the measurement apparatus. MIPTs lie outside established paradigms for equilibrium or nonequilibrium critical phenomena and delineate distinct, stable dynamical and computational phases of matter. quantum computers enable programmable simulation of the interaction of a measurement apparatus with a dynamical quantum system, to explore MIPT phenomena over a range of system sizes while retaining quantum coherence. Yet, existing experimental protocols rely on fundamentally nonscalable postselection techniques or direct classical simulation of quantum circuits. Here, we report the scalable observation of finite-size scaling evidence for an observable-sharpening MIPT in monitored quantum circuits in a chain of Yb+171 ions in Quantinuum’s H1-1 trapped-ion quantum processor. By leveraging an equivalent description as a statistical physics problem, we implement scalable classical algorithms to infer the value of the measured observable from a single experimental shot. This technique enables a truly scalable protocol to observe observable-sharpening MIPTs in generic classes of circuits that cannot be directly classically simulated and also provides enhanced means to detect and suppress errors in the quantum simulation.

关键词： Dynamical phase transitions quantum circuits quantum entanglement quantum measurements quantum phase transitions

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Degradability of Modified Landau-Streater Type Low-Noise quantum Channels in High Dimensions

arXiv

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arXiv 2024年

作者： Lo, Yun-Feng Lee, Yen-Chi Hsieh, Min-Hsiu Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Quantum Computing Research Center Hon-Hai Research Institute Taipei Taiwan

This paper delves into the degradability of quantum channels, with a specific focus on high-dimensional extensions of qubit depolarizing channels in low-noise regimes. We build upon the foundation of η-approximate degradable channels, as established by Sutter et al. and Leditzky et al., to introduce and examine the Modified Landau-Streater (MLS) channels. These channels expand upon the qubit depolarizing and the recently proposed modified Werner-Holevo channels by Roofeh and Karimipour, extending them to higher-dimensional Hilbert spaces (with dimension d = 2j + 1, where j are positive half-integers). Our investigation centers on their conformity to the O(Ε2) degradability pattern, aligning with and extending Leditzky et al.’s findings in the d = 2 case. By replacing the SU(2) generators with SU(d) in our treatment, we may explore the potential inclusion of generalized Gell-Mann matrices in future research. Our results enhance the understanding of super-additivity in quantum channels within the low-noise regime and lay the groundwork for future explorations into conditions and structures that could lead to O(Ε2) degradability across a broader spectrum of quantum channels. © 2024, CC BY.

关键词： quantum entanglement

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Flexible computational photodetectors for self-powered activity sensing

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npj Flexible Electronics 2022年第1期6卷 45-52页

作者： Dingtian Zhang Canek Fuentes-Hernandez Raaghesh Vijayan Yang Zhang Yunzhi Li Jung Wook Park Yiyang Wang Yuhui Zhao Nivedita Arora Ali Mirzazadeh Youngwook Do Tingyu Cheng Saiganesh Swaminathan Thad Starner Trisha L.Andrew Gregory D.Abowd School of Interactive Computing Georgia Institute of TechnologyAtlantaGAUSA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGAUSA Department of Electrical and Computer Engineering Northeastern UniversityBostonMAUSA Department of Chemistry University of Massachusetts AmherstAmherstMAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCAUSA Human-Computer Interaction Institute Carnegie Mellon UniversityPittsburghPAUSA Departments of Chemistry and Chemical Engineering University of Massachusetts AmherstAmherstMAUSA

Conventional vision-based systems,such as cameras,have demonstrated their enormous versatility in sensing human activities and developing interactive ***,these systems have long been criticized for incurring privacy,power,and latency issues due to their underlying structure of pixel-wise analog signal acquisition,computation,and *** this research,we overcome these limitations by introducing in-sensor analog computation through the distribution of interconnected photodetectors in space,having a weighted responsivity,to create what we call a computational *** photodetectors can be used to extract mid-level vision features as a single continuous analog signal measured via a two-pin *** develop computational photodetectors using thin and flexible low-noise organic photodiode arrays coupled with a self-powered wireless system to demonstrate a set of designs that capture position,orientation,direction,speed,and identification information,in a range of applications from explicit interactions on everyday surfaces to implicit activity detection.

关键词： everyday overcome photodetector

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm 2

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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2nd IEEE International Conference on Trends in quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mayuri, A.V.R. Selvekumar Subash, K. Aeron, Anurag Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Bahir Dar University Bahir Dar Institute of Technology Department of Electrical and Computer Engineering Ethiopia Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350384277

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness. © 2024 IEEE.

关键词： Convolutional neural networks

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