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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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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

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CauSkelNet: Causal Representation Learning for Human Behaviour Analysis

arXiv

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arXiv 2024年

作者： Gu, Xingrui Jiang, Chuyi Wang, Erte Wu, Zekun Cui, Qiang Tian, Leimin Wu, Lianlong Song, Siyang Yu, Chuang EECS University of California Berkeley BerkeleyCA United States Department of Electrical Engineering Columbia University New YorkNY United States Department of Computer Science University College London London United Kingdom Holistic AI University College London London United Kingdom The Future Laboratory Tsinghua University Beijing China School of Electrical and Computer Systems Engineering Monash University Melbourne Australia Department of Computer Science Oxford University Oxford United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom UCL Interaction Centre University College London London United Kingdom

Constrained by the lack of model interpretability and a deep understanding of human movement in traditional movement recognition machine learning methods, this study introduces a novel representation learning method based on causal inference to better understand human joint dynamics and complex behaviors. We propose a two-stage framework that combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between joints. Our method effectively captures interactions and produces interpretable, robust representations. Experiments on the EmoPain dataset show that our causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, especially in detecting protective behaviors. The model is also highly invariant to data scale changes, enhancing its reliability in practical applications. Our approach advances human motion analysis and paves the way for more adaptive intelligent healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Adaptive Softmax Regression for Credit Scoring 16th

Adaptive Softmax Regression for Credit Scoring

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Munkhdalai, Lkhagvadorj Davagdorj, Khishigsuren Pham, Van-Huy Ryu, Keun Ho Database/Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789813367562

Credit scoring is a classification task from the machine learning perspective. Efficiently classifying bad borrowers is the main aim of building a credit scoring model. This work proposes a novel adaptive softmax regression method for credit scoring. We augment a simple softmax regression by deep neural networks to make its estimated probabilities as an adaptive for each observation. Our experimental result on public benchmark datasets shows that adaptive softmax regression outperformed the machine learning baselines in terms of Brier score, area under the curve (AUC) and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Decision making

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Optimizing Community Retailer Pricing and Residential Electricity Consumption Considering Carbon Trading Mechanism

SSRN

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SSRN 2024年

作者： Zhang, Xiaohua Deng, Kuiheng Li, Yifu Dehghanian, Payman Chen, Xingrui Chen, Bolin Lyu, Junyan School of Mechanical Engineering and Rail Transit Changzhou University Jiangsu Changzhou213614 China Department of Electrical and Computer Engineering The George Washington University WashingtonDC20052 United States Jiangsu Key Laboratory of Green Process Equipment Jiangsu Changzhou213614 China

With the popularization of community-based distributed power generation, managing residents’ electricity consumption has become more challenging. This paper introduces an optimization strategy for community retailer pricing and residential electricity consumption to achieve a community energy management policy, taking into account the ladder-type carbon trading mechanism. Firstly, mathematical models are formulated for the community equipment and the residential electricity consumption. This includes a leader-follower game model grounded in Stackelberg game theory principles and a ladder-type carbon trading mechanism designed for residents. The community utilizes this carbon trading mechanism to produce real-time carbon prices for electricity consumption, with these prices being influenced by the constraints of both the ladder-type carbon trading mechanism and varying levels of carbon emission intensity (CEI). The carbon pricing is factored into the electricity prices for the *** retailer aims to maximize its revenue, while the community residents aim to minimize their electricity costs. Subsequently, an optimization problem is formulated on differential evolution (DE), particle swarm optimization, and improved particle swarm optimization (IPSO). The efficacy of the proposed model is numerically investigated through real-world data on a community in China. © 2024, The Authors. All rights reserved.

关键词： Costs

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MULTI-FREQUENCY NMM3D SIMULATIONS OF WAVE PROPAGATION IN VEGETATION FOR REMOTE SENSING OF SOIL MOISTURE

MULTI-FREQUENCY NMM3D SIMULATIONS OF WAVE PROPAGATION IN VEG...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Gu, Weihui Tsang, Leung Colliander, Andreas Yueh, Simon Radiation Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

ISBN: (纸本)9781665403696

To investigate the feasibility of using multi-frequency to further improve the soil moisture retrieval, the recently developed hybrid method is used to perform full-wave simulations of a wheat field at three different frequencies of L-, S-, and C-bands. In the hybrid method, the multiple scattering within a single wheat plant is first captured using the T-matrix based on the full-wave solutions of HFSS. In the second step, the scatterings among different plants are considered using the Foldy-Lax equations of multiple scattering theory (MST). The transmission of microwaves through wheat field at the L-, S- and C-bands are calculated using the hybrid method. Results show that: (1) the transmission obtained from the full-wave simulations is much larger than those computed from the radiative transfer equations (RTE) model, (2) the hybrid method transmission has a weaker frequency dependence than those of the RTE, and (3) the attenuation caused by the vegetation layer would saturate with an increase in frequency. © 2021 IEEE

关键词： Frequency dependence C-band Computational modeling Soil moisture Scattering Vegetation mapping Microwave theory and techniques

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Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-29页

作者： Chang, Chein-I Lin, Chien-Yu Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite that a recently developed effective anomaly space (EAS) was designed to use data sphering (DS) to remove the second-order data statistics characterized by BKG, enhance anomaly detectability, and reduce noise effect, it does not address the IBCR issue. To cope with this issue, this article introduces band sampling (BSam) into EAS to reduce IBCR and further suppress BKG more effectively. By implementing EAS in conjunction with BSam (EAS-BSam), these four issues can be resolved altogether for any arbitrary anomaly detector. It first modifies iterative spectral-spatial hyperspectral anomaly detection (ISSHAD) to develop a new variant of ISSHAD, called iterative spectral-spatial maximal map (ISSMax), and then generalizes ISSMax to EAS-ISSMax, which further enhances anomaly detectability and noise removal. Finally, EAS-BSam is implemented to reduce IBCR. As a result, combining EAS, BSam, and ISSMax yields four versions: EAS-BSam, EAS-ISSMax, BSam-SSMAX, and EAS-BSam-SSMax. Such integration presents a great challenge because all these four versions are derived from different aspects, iterative spectral-spatial feedback process, compressive sensing, and low-rank and sparse matrix decomposition. Experiments demonstrate that EAS-BSam and EAS-BSam-SSMax show their superiority to ISSHAD and many current existing hyperspectral anomaly detection (HAD) methods. © 1980-2012 IEEE.

关键词： Iterative methods

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TiO2:GeO2 and SiO2 Coating with Low Thermal Noise for Gravitational Wave Detectors

TiO2:GeO2 and SiO2 Coating with Low Thermal Noise for Gravit...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Aaron Davenport Gabriele Vajente Nicholas Demos GariLynn Billingsley Ashot Markosyan Riccardo Bassiri Martin M. Fejer Slawek Gras Matthew Evans Peter Fristchel Carmen S. Menoni Department of Electrical Computer Engineering and Chemistry Colorado State University Fort Collins CO USA LIGO Laboratory California Institute of Technology Pasadena California USA LIGO Laboratory Massachusetts Institute of Technology Cambridge MA E. L. Ginzton Laboratory Stanford University Stanford California USA

TiO 2 :GeO 2 alloy and SiO 2 thin film high reflector stacks with low coating thermal noise and excellent optical properties are demonstrated. The effect of design and annealing of the coatings is studied.

ISBN: (纸本)9798350369311

关键词： Silicon compounds Laser noise Metals Gravitational waves Optical detectors Thermal noise Coatings Optical reflection Optical noise Optical films

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A Subtype Classification of Hematopoietic Cancer Using Machine Learning Approach 13th

A Subtype Classification of Hematopoietic Cancer Using Machi...

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13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021

作者： Park, Kwang Ho Pham, Van Huy Davagdorj, Khishigsuren Munkhdalai, Lkhagvadorj Ryu, Keun Ho Database and Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811616846

Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which are usually difficult to detect in the hematopoiesis process. For this reason, we focused on the five subtypes of hematopoietic cancer and conducted a study on classifying by applying machine learning algorithms both contextual approach and non-contextual approach. First, we applied PCA approach for extracting suited feature for building classification model for subtype classification. And then, we used four machine learning classification algorithms (support vector machine, k-nearest neighbor, random forest, neural network) and synthetic minority oversampling technique for generating a model. As a result, most classifiers performed better when the oversampling technique was applied, and the best result was that oversampling applied random forest produced 95.24% classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Gene expression

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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