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Routing Single Photons from a Trapped Ion Using a Photonic Integrated Circuit

Physical Review applied 2023年第3期19卷 034001-034001页

作者： Uday Saha James D. Siverns John Hannegan Mihika Prabhu Qudsia Quraishi Dirk Englund Edo Waks Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Research in Electronics and Applied Physics (IREAP) University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA United States Army Research Laboratory Adelphi Maryland 20783 USA Joint Quantum Institute (JQI) University of Maryland College Park Maryland 20742 USA

Trapped ions are promising candidates for nodes of a scalable quantum network due to their long-lived qubit coherence times and high-fidelity single- and two-qubit gates. Future quantum networks based on trapped ions will require a scalable way to route photons between different nodes. Photonic integrated circuits from fabrication foundries provide a compact solution to this problem. However, these circuits typically operate at telecommunication wavelengths that are incompatible with the strong dipole emissions of trapped ions. In this work, we demonstrate the routing of single photons from a trapped ion using a photonic integrated circuit. We employ quantum frequency conversion to match the emission of the ion to the operating wavelength of a foundry-fabricated silicon nitride photonic integrated circuit, achieving a total transmission of 31.0% ± 0.9% through the device. Using programmable phase shifters, we switch the single photons between the output channels of the circuit and demonstrate a 50:50 beam splitting condition. These results constitute an important step towards programmable routing and entanglement distribution in large-scale quantum networks and distributed quantum computers.

关键词： Integrated optics Optical quantum information processing Quantum communication Quantum information with trapped ions Quantum interconnects Quantum networks Trapped ions

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Updating high average-utility itemsets with pre-large concept

Updating high average-utility itemsets with pre-large concep...

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作者： Wu, Jimmy Ming-Tai Teng, Qian Lin, Jerry Chun-Wei Yun, Unil Chen, Hsing-Chung College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Science and Information Engineering Asia University Taichung Taiwan

HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strategies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Maintenance

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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IEEE International Conference on Big Data

作者： Mohammad Masum Nafisa Anjum Md Jobair Hossain Faruk Hossain Shahriar Nazmus Sakib Maria Valero M.A. Karim Akond Rahman Fan Wu Alfredo Cuzzocrea Department of Applied Data Science San Jose State University San Jose USA Department of Information Technology Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Civil and Environmental Engineering Kennesaw State University USA Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480468

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country’s social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country’s 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2 nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score.

关键词： Bridges Decision making Transportation Machine learning Maintenance engineering Predictive models Big Data

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Arabic Documents Clustering using Bond Energy Algorithm and Genetic Algorithm

Arabic Documents Clustering using Bond Energy Algorithm and ...

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International Conference on Information and Communication Systems (ICICS)

作者： Hadeel Alazzam Orieb AbuAlghanam Abdulsalam Alsmady Esra’ a Alhenawi Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Networks and Information Security Alahliyya Amman University Amman Jordan Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science University of Jordan Amman Jordan

Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the Bond Energy Algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.

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Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support

arXiv

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arXiv 2022年

作者： Mohammadian, Mostafa Baker, Kyri Fioretto, Ferdinando The College of Engineering and Applied Science University of Colorado Boulder BoulderCO80309 United States The Electrical Engineering and Computer Science Department Syracuse University SyracuseNY13244 United States

The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results. However, power system dynamics are not snap-shot, steady-state operations. These dynamics must be considered to ensure that the optimal solutions provided by these models adhere to practical dynamical constraints, avoiding frequency fluctuations and grid instabilities. Unfortunately, dynamic system models based on ordinary or partial differential equations are frequently unsuitable for direct application in control or state estimates due to their high computational costs. To address these challenges, this paper introduces a machine learning method to approximate the behavior of power systems dynamics in near real time. The proposed framework is based on gradient-enhanced physics-informed neural networks (gPINNs) and encodes the underlying physical laws governing power systems. A key characteristic of the proposed gPINN is its ability to train without the need of generating expensive training data. The paper illustrates the potential of the proposed approach in both forward and inverse problems in a single-machine infinite bus system for predicting rotor angles and frequency, and uncertain parameters such as inertia and damping to showcase its potential for a range of power systems applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Ordinary differential equations

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An explainable deep learning approach for stock market trend prediction

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Heliyon 2024年第21期10卷 e40095页

作者： Muhammad, Dost Ahmed, Iftikhar Naveed, Khwaja Bendechache, Malika CRT-AI and ADAPT Research Centres School of Computer Science University of Galway Ireland Department of Software Engineering University of Europe for Applied Sciences Potsdam Germany Swedish Center for Digital Innovation University of Gothenburg Sweden ADAPT Research Centre School of Computer Science University of Galway Ireland

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.9%, compared to 85.7% for random forest, 60.07% for support vector machine, and 52.45% for logistic regression. Furthermore the proposed model excels in F1-score, with a 94.85% performance, compared to 77.95% for random forest, 21.02% for support vector machine and 46.23% for logistic regression, across four real world diverse datasets. Additionally, we employ explainable AI (XAI) techniques, SHAP and LIME, to enhance interpretability, enabling stakeholders to understand the key factors driving predictions. The SHAP analysis reveals the top 10 most important/influential features, enabling feature reduction while maintaining performance. Interestingly, while accuracy slightly decreases with top 10 features, precision, recall, and F1-score improve, suggesting a trade-off between comprehensiveness and performance. These results demonstrate the potential for practical application in financial decision-making, providing a balance between interpretability and predictive power that can support investors in risk management and strategic planning. © 2024 The Author(s)

关键词： Deep neural network Explainable AI Machine learning Stock market trend prediction Time series prediction

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Forex exchange using big data analytics 1

Forex exchange using big data analytics

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Arun, M. Deepa, R. Muthukumaran, V. Lokesh Kumar, R. Prasanna Kumar, R.B. Vellore Institute of Technology Vellore India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Telangana Hyderabad India Department of Mathematics School of Applied Science REVA University Bengaluru64 India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore632 014 India Department of Computer Science REVA University Bengaluru64 India

Analysis and Prediction of forex has gained immense value in today's economy. The stock price prediction is a difficult process owing to the irregularities in stock prices. Every trader wants to know if the pattern has been repeated in past to know what the possible output of the current situation will be. The primary objective is to propose a methodology that will use a historical dataset and provide a more accurate prediction on stock price. In this paper, we will be using machine learning pattern recognition algorithm on forex tick dataset. The learned model then will produce pattern from the given dataset and on the pattern of increasing or decreasing, the buyer will initiate a buy or sell the stock respectively. We will use python coding to execute the algorithm in jupyter notebook. Matplot library will help us to perform graphing in the process and Numpy will be helpful in doing statistical analysis of data. © Published under licence by IOP Publishing Ltd.

关键词： Python

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Measuring Large Language Models Capacity to Annotate Journalistic Sourcing

arXiv

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arXiv 2024年

作者： Vincent, Subramaniam Wang, Phoebe Shi, Zhan Koka, Sahas Fang, Yi Markkula Center for Applied Ethics Santa Clara University Santa ClaraCA United States Department of Computer Science and Engineering Santa Clara University Santa ClaraCA United States Dublin High School DublinCA United States

Since the launch of ChatGPT in late 2022, the capacities of Large Language Models and their evaluation have been in constant discussion and evaluation both in academic research and in the industry. Scenarios and benchmarks have been developed in several areas such as law, medicine and math (Bommasani et al., 2023) and there is continuous evaluation of model variants. One area that has not received sufficient scenario development attention is journalism, and in particular journalistic sourcing and ethics. Journalism is a crucial truth-determination function in democracy (Vincent, 2023), and sourcing is a crucial pillar to all original journalistic output. Evaluating the capacities of LLMs to annotate stories for the different signals of sourcing and how reporters justify them is a crucial scenario that warrants a benchmark approach. It offers potential to build automated systems to contrast more transparent and ethically rigorous forms of journalism with everyday fare. In this paper we lay out a scenario to evaluate LLM performance on identifying and annotating sourcing in news stories on a five-category schema inspired from journalism studies (Gans, 2004). We offer the use case, our dataset and metrics and as the first step towards systematic benchmarking. Our accuracy findings indicate LLM-based approaches have more catching to do in identifying all the sourced statements in a story, and equally, in matching the type of sources. An even harder task is spotting source justifications. Copyright © 2024, The Authors. All rights reserved.

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High-purity single-photon generation based on cavity QED

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Physical Review Research 2025年第1期7卷 013251-013251页

作者： Seigo Kikura Rui Asaoka Masato Koashi Yuuki Tokunaga Department of Applied Physics Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo 169-8585 Japan Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Photon Science Center Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

We propose a scheme for generating a high-purity single photon on the basis of cavity QED. This scheme employs an atom as a four-level system and the structure allows the suppression of the reexcitation process due to the atomic decay, which is known to significantly degrade the single-photon purity in state-of-the-art photon sources using a three-level system. Our analysis shows that the reexcitation probability arbitrarily approaches zero without sacrificing the photon generation probability when increasing the power of a driving laser between two excited states. This advantage is achievable by using current cavity-QED technologies. Our scheme can contribute to developing distributed quantum computation or quantum communication with high accuracy.

关键词： Quantum computers

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A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classifications

A Parameterized Generative Adversarial Network Using Cyclic ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiangyu Xiong Yue Sun Xiaohong Liu Chan-Tong Lam Tong Tong Hao Chen Qinquan Gao Wei Ke Tao Tan Faculty of Applied Sciences Macao Polytechnic University John Hopcroft Center (JHC) for Computer Science Shanghai Jiao Tong University College of Physics and Information Engineering Fuzhou University Department of Mathware Jiangsu JITRI Sioux Technologies Co. Ltd

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

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