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Heralded nonlocal quantum gates for distributed quantum computation in a decoherence-free subspace

Physical Review A 2024年第5期110卷 052612-052612页

作者： Wanhua Su Wei Qin Adam Miranowicz Tao Li Franco Nori MIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing School of Physics Nanjing University of Science and Technology Nanjing 210094 China Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Tianjin 300350 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama 351-0198 Japan Institute of Spintronics and Quantum Information Faculty of Physics and Astronomy Adam Mickiewicz University PL-61-614 Poznań Poland Engineering Research Center of Semiconductor Device Optoelectronic Hybrid Integration in Jiangsu Province Nanjing 210094 China Quantum Information Physics Theory Research Team Quantum Computing Center RIKEN Wako-shi Saitama 351-0198 Japan Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA

We propose a heralded protocol for implementing nontrivial quantum gates on two stationary qubits coupled to spatially separated cavities. By dynamically controlling the evolution of the composite system, nonlocal two-qubit quantum (e.g., cphase and cnot) gates can be achieved without real excitations of either cavity modes or atoms. The success of our protocol is conditioned on projecting an auxiliary atom onto a postselected state, which simultaneously removes various detrimental effects of dissipation on the gate fidelity. In principle, the success probability of the gate can approach unity as the single-atom cooperativity becomes sufficiently large. Furthermore, we show its application for implementing single- and two-qubit gates within a decoherence-free subspace that is immune to a collective dephasing noise. This faithful, heralded, and nonlocal protocol could, therefore, be useful for distributed quantum computation and scalable quantum networks.

关键词： Quantum computation Quantum gates Quantum networks Quantum repeaters

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STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

arXiv

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arXiv 2021年

作者： Ghosh, Shreya Mukherjee, Anwesha Ghosh, Soumya K. Buyya, Rajkumar Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science Mahishadal Raj College West Bengal India Laboratory School of Computing and Information Systems University of Melbourne Australia

Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones;and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. © 2021, CC BY.

关键词： Coronavirus

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CDOpt: A Python Package for a Class of Riemannian Optimization

arXiv

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arXiv 2022年

作者： Xiao, Nachuan Hu, Xiaoyin Liu, Xin Toh, Kim-Chuan The Institute of Operations Research and Analytics National University of Singapore Singapore School of Computer and Computing Science Hangzhou City University Hangzhou China Academy of Edge Intelligence Hangzhou City University Gongshu Hangzhou China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore

Optimization over an embedded submanifold defined by equality constraints c(x) = 0 has attracted much interest over the past few decades due to its wide applications in various areas, including computer vision, signal processing, numerical linear algebra, and deep learning. For solving such problems, many related optimization packages have been developed based on Riemannian optimization approaches, which rely on some basic geometrical materials of Riemannian manifolds, including Riemannian gradients, retractions, vector transports, etc. These geometrical materials can be challenging to determine in general. In fact, existing packages only accommodate a few well-known manifolds whose geometrical materials are more easily accessible. For other manifolds that are not contained in these packages, the users have to develop the geometric materials by themselves. In addition, it is not always tractable to adapt the advanced features from various state-of-the-art unconstrained optimization solvers to Riemannian optimization approaches. Here we introduce a user-friendly Python package, CDOpt (available at https: //***/ under BSD 3-clause license), for solving a class of Riemannian optimization problems. CDOpt is designed to complement existing Riemannian optimization packages by transforming Riemannian optimization problems into their unconstrained counterparts through the constraint dissolving approach. We prove that the when the penalty parameter in the constraint dissolving approach is sufficiently large, Riemannian optimization problems and their unconstrained counterparts are equivalent. Therefore, solving Riemannian optimization problems through CDOpt can directly benefit from various existing solvers and the rich expertise gained over the past few decades for unconstrained optimization. Moreover, all the computations in CDOpt related to any manifold in question are conducted through its constraints expression, hence users can easily define new manifolds in

关键词： Constrained optimization

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Multimodal Fusion of Speech and Gesture Recognition based on Deep Learning 2

Multimodal Fusion of Speech and Gesture Recognition based on...

引用

2019 2nd International Conference on Computer Information science and Artificial Intelligence, CISAI 2019

作者： Qiu, Xiaoyu Feng, Zhiquan Yang, Xiaohui Tian, Jinglan College of Information Science and Engineering University of Jinan Jinan Shandong250022 China Shandong Provincial Key Laboratory of Network Environment Intelligent Computing Technology University of Jinan Jinan Shandong250022 China

This paper proposes a multimodal fusion architecture based on deep learning. The architecture consists of two forms: speech command and hand gesture. First, the speech and gesture commands input by users are recognized by CNN for speech command recognition and LSTM for hand gesture recognition respectively. Secondly, the obtained results are searched by keywords and compared by similarity degree to obtain recognition results. Finally, the two results are fused to output the final instructions. Experiments show that the proposed multi-mode fusion model is superior to the single-mode fusion model. © Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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A Machine Learning Approach to Biomarker Screening on Proteomics Data of Cleft Lip and Palate 2020

A Machine Learning Approach to Biomarker Screening on Proteo...

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The Fourth International Symposium on Image computing and Digital Medicine

作者： Weidong Xie Linjie Wang Kun Yu Wei Li School of Computer Science and Engineering Northeastern University China College of medicine and bioinformation engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image (MIIC) Northeastern University Ministry of Education China

ISBN: (纸本)9781450389686

Genomic and proteomic techniques have provided new research ideas for the early diagnosis of diseases such as cleft lip and palate (CLP). However, a significant challenge is finding the best set of biomarkers for the disease’s clinical diagnosis in massive or high dimensions. Existing studies have focused on refinements and combinations of feature selection methods to improve classification accuracy without interpreting the results. In this paper, seven feature selection methods are firstly compared on eight publicly available genomics microarray data. Moreover, five classification models and two pre-processing methods are aslo compared to find the most appropriate method for processing the microarray data. The method is finally validated on a CLP dataset. The results indicate that the methods described herein can achieve high classification accuracy and provide better feature interpretation.

关键词： cleft lip and palate feature selection Genomics clinical interpretation

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The all-particle spectrum and mean logarithmic mass of cosmic rays in the knee region by LHAASO-KM2A 38

The all-particle spectrum and mean logarithmic mass of cosmi...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Hengying He, Huihai Feng, Cunfeng Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facu

The kilometer-square array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO, located at 4410 m above sea level with an atmospheric depth of 600 g/cm2) can simultaneously measure air shower sizes of both electromagnetic particles and muon contents with high precision for cosmic rays with energies in the knee region. The energy is reconstructed by combining parameters of muons and electromagnetic particles, which is weakly dependent on the mass composition of cosmic rays. We study the measurement method of the all particle spectrum and mean logarithmic mass through the simulation data, and discuss the corresponding systematic errors. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Systematic errors

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Projections from D2 Neurons in Different Subregions of Nucleus Accumbens Shell to Ventral Pallidum Play Distinct Roles in Reward and Aversion

引用

Neuroscience Bulletin 2021年第5期37卷 623-640页

作者： Yun Yao Ge Gao Kai Liu Xin Shi Mingxiu Cheng Yan Xiong Sen Song Laboratory of Brain and Intelligence Department of Biomedical Engineeringand McGovern Institute for Brain ResearchTsinghua UniversityBeijing 100084China Center for Brain-Inspired Computing Research Beijing Innovation Center for Future ChipsTsinghua UniversityBeijing 100084China School of Life Science Tsinghua UniversityBeijing 100084China National Institute of Biological Sciences Beijing 102206 Department of Psychology University of MichiganAnn ArborMI 48109USA Neuroscience Research Institute and Department of Neurobiology School of Basic Medical SciencesPeking UniversityBeijing 100191China

The nucleus accumbens shell(NAcSh) plays an important role in reward and aversion. Traditionally, NAc dopamine receptor 2-expressing(D2) neurons are assumed to function in aversion. However, this has been challenged by recent reports which attribute positive motivational roles to D2 neurons. Using optogenetics and multiple behavioral tasks, we found that activation of D2 neurons in the dorsomedial NAcSh drives preference and increases the motivation for rewards, whereas activation of ventral NAcSh D2 neurons induces aversion. Stimulation of D2 neurons in the ventromedial NAcSh increases movement speed and stimulation of D2 neurons in the ventrolateral NAc Sh decreases movement speed. Combining retrograde tracing and in situ hybridization, we demonstrated that glutamatergic and GABAergic neurons in the ventral pallidum receive inputs differentially from the dorsomedial and ventral NAcSh. All together, these findings shed light on the controversy regarding the function of NAcSh D2 neurons, and provide new insights into understanding the heterogeneity of the NAcSh.

关键词： Nucleus accumbens shell Ventral pallidum D2 neurons Reward Aversion Motivation

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Ordering-based causal discovery with reinforcement learning

arXiv

引用

arXiv 2021年

作者： Wang, Xiaoqiang Du, Yali Zhu, Shengyu Ke, Liangjun Chen, Zhitang Hao, Jianye Wang, Jun State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi'an Jiaotong University University College London Huawei Noah's Ark Lab College of Intelligence and Computing Tianjin University

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a multi-step Markov decision process, implement the ordering generating process with an encoder-decoder architecture, and finally use RL to optimize the proposed model based on the reward mechanisms designed for each ordering. A generated ordering would then be processed using variable selection to obtain the final causal graph. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training. Experimental results on both synthetic and real data sets shows that the proposed method achieves a much improved performance over existing RL-based method. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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StateConsisIV: A privacy-preserving integrity verification method for cloud components based on a novel state consistency feature 5

StateConsisIV: A privacy-preserving integrity verification m...

引用

5th IEEE International Conference on Data science in Cyberspace, DSC 2020

作者： Fan, Peiru Wang, Chonghua Li, Jun Zhao, Bo Ji, Zhaoxu China Industrial Control Systems Cyber Emergency Response Team Beijing China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering WuHan China

ISBN: (纸本)9781728195582

Plain proofs (e.g., raw logs, report, etc.) are significant and effective for integrity verification. In our analysis and comparison of existing work, we found most of them did not employ any protection mechanisms on the proofs. However, these proofs contain sensitive information, which may cause privacy leakage risks when the third party verifier (TPV) is compromised. The situation is even worse when the verification objects are cloud components. Motivated by this, we present StateConsisIV, a privacy-preserving integrity verification method based on a novel state consistency feature to address the privacy leakage problem. The core idea of our work is to enable the integrity judgment through encrypted proofs, withholding plain proofs inside the cloud only to reduce attack surface and enhance privacy. In specific, we employ random transformation algorithm on cloud nodes to encrypt proofs on their birth places. Besides, we design a novel state consistency feature based on the deployment and operation pattern of structural cloud components and perform feature analysis on TPV to guarantee an accurate integrity judgment result. We evaluate our approach on one typical dataset. The experimental results show that our method is considered more worthy with a little bit of extra computation overhead. © 2020 IEEE.

关键词： Privacy-preserving techniques

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Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction

arXiv

引用

arXiv 2022年

作者： Rao, Jiahua Zheng, Shuangjia Mai, Sijie Yang, Yuedong School of Computer Science and Engineering Sun Yat-Sen University China School of Electronic and Information Technology Sun Yat-Sen University China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University China Galixir Technologies Ltd Beijing China

Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existing methods either heavily rely on high-quality domain features or are intrinsically transductive, which limits the capacity of models to generalize to drugs/genes that lack external information or are unseen during the training process. To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features. Moreover, the model strengthened the relations on the drug-gene graph through a communicative message passing mechanism. To evaluate our method, we compiled two new benchmark datasets from DrugBank and DGIdb. The comprehensive experiments on the two datasets showed that our method outperformed state-of-the-art baselines in the transductive scenarios and achieved superior performance in the inductive ones. Further experimental analysis including LINCS experimental validation and literature verification also demonstrated the value of our model. © 2022, CC BY-NC-ND.

关键词： Genes

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