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Gaussian Boson Sampling with Pseudo-Photon-Number-Resolving Detectors and Quantum Computational Advantage

Physical Review Letters 2023年第15期131卷 150601-150601页

作者： Yu-Hao Deng Yi-Chao Gu Hua-Liang Liu Si-Qiu Gong Hao Su Zhi-Jiong Zhang Hao-Yang Tang Meng-Hao Jia Jia-Min Xu Ming-Cheng Chen Jian Qin Li-Chao Peng Jiarong Yan Yi Hu Jia Huang Hao Li Yuxuan Li Yaojian Chen Xiao Jiang Lin Gan Guangwen Yang Lixing You Li Li Han-Sen Zhong Hui Wang Nai-Le Liu Jelmer J. Renema Chao-Yang Lu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and School of Physical Sciences University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Micro system and Information Technology (SIMIT) Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China Department of Computer Science and Technology and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China Adaptive Quantum Optics Group Mesa+ Institute for Nanotechnology University of Twente P.O. Box 217 7500 AE Enschede Netherlands New Cornerstone Science Laboratory Shenzhen 518054 China

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical spoofing mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ∼600 yr using exact methods, whereas our quantum computer, Jiǔzhāng 3.0, takes only 1.27 μs to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier∼3.1×1010 yr.

关键词： Boson sampling Quantum computation Quantum information with atoms & light Quantum states of light Photon counting

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KdV-like soliton gas: similarity and difference in integrable and non-integrable models

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Physica D: Nonlinear Phenomena 2025年 481卷

作者： Ekaterina Didenkulova Marcelo V. Flamarion Efim Pelinovsky Faculty of Informatics Mathematics and Computer Science HSE University Nizhny Novgorod 603155 Russia A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences Nizhny Novgorod 603155 Russia

A comparison of the statistical characteristics of a rarefied soliton gas is carried out within the framework of integrable and non-integrable equations from the Korteweg-de Vries (KdV) hierarchy. As examples, multi-soliton solutions of the modified KdV equation, and the modular Schamel equation are considered. A common property of the dynamics of bipolar solitons is the formation of rogue waves, which do not occur in unipolar gases. The fourth moment of the wave field (kurtosis) increases compared to the initial value in the case of a bipolar gas, and decreases for a unipolar gas. In the case of integrable KdV equations, the characteristics of the soliton gas reach a stationary level, while in non-integrable equations they remain functions of time. The inelastic transfer of energy from small solitons to large ones occur, and large waves become “more extreme” against the background of small solitons. The tendency of the occurrence of an anomalously large wave (soliton - champion) in non-integrable systems are discussed.

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Post-Error Correction for Quantum Annealing Processor using Reinforcement Learning

arXiv

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arXiv 2022年

作者： Śmierzchalski, Tomasz Pawela, Lukasz Puchala, Zbigniew Trzcinski, Tomasz Gardas, Bartlomiej Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland

Finding the ground state of the Ising spin-glass is an important and challenging problem (NP-hard, in fact) in condensed matter physics. However, its applications spread far beyond physic due to its deep relation to various combinatorial optimization problems, such as travelling salesman or protein folding. Sophisticated and promising new methods for solving Ising instances rely on quantum resources. In particular, quantum annealing is a quantum computation paradigm, that is especially well suited for Quadratic Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available quantum annealers (i.e., D-Wave) are prone to various errors, and their ability to find low energetic states (corresponding to solutions of superior quality) is limited. This naturally calls for a post-processing procedure to correct errors (capable of lowering the energy found by the annealer). As a proof-of-concept, this work combines the recent ideas revolving around the DIRAC architecture with the Chimera topology and applies them in a real-world setting as an error-correcting scheme for quantum annealers. Our preliminary results show how to correct states output by quantum annealers using reinforcement learning. Such an approach exhibits excellent scalability, as it can be trained on small instances and deployed for large ones. However, its performance on the chimera graph is still inferior to a typical algorithm one could incorporate in this context, e.g., simulated annealing. © 2022, CC BY.

关键词： Quantum theory

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Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients

Applied Bionics and Biomechanics

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Applied Bionics and Biomechanics 2021年 2021卷 9338091页

作者： Maabreh, Roqia Saleem Awad Alazzam, Malik Bader Alghamdi, Ahmed S. Prince Al Hussein Bin Abdulla Academy for Civil Protection Jordan Faculty of Computer Science and Informatics Amman Arab University Jordan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia

Today, cancer is the second leading cause of death worldwide, and the number of people diagnosed with the disease is expected to rise. Breast cancer is the most commonly diagnosed cancer in women, and it has one of the highest survival rates when treated properly. Because the effectiveness and, as a result, survival of the patient are dependent on each case, it is critical to know the modelling of their survival ahead of time. Artificial intelligence is a rapidly expanding field, and its clinical applications are following suit (having surpassed humans in many evidence-based medical tasks). From the inception of since first stable risk estimator based on statistical methods appeared in survival analysis, there have been numerous versions of it created, with machine learning being used in only a few of them. Nonlinear relationships between variables and the impact they have on the variable to be predicted are very easy to evaluate using statistical methods. However, because they are just mathematical equations, they have flaws that limit the quality of their output. The main goal of this study is to find the best machine learning algorithms for predicting the individualised survival of breast cancer patients, as well as the most appropriate treatment, and to propose new numerical variable stratifications. They will still be carried out using unsupervised machine learning methods that divide patients into groups based on their risk in each dataset. We will compare it to standard groupings to see if it has more significance. Knowing that the greatest challenge in dealing with clinical data is its quantity and quality, we have gone to great lengths to ensure their quality before replicating them. We used the Cox statistical method in conjunction with other statistical methods and tests to find the best possible dataset with which to train our model, despite its ease of multivariate analysis. © 2021 Roqia Saleem Awad Maabreh et al.

关键词： Statistical tests

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Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

arXiv

引用

arXiv 2022年

作者： Han, Husheng Hu, Xing Xu, Kaidi Dang, Pucheng Wang, Ying Zhao, Yongwei Du, Zidong Guo, Qi Yang, Yanzhi Chen, Tianshi SKL of Processors Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cambricon Technologies Beijing China Department of Computer Science College of Computing & Informatics Drexel University Philadelphia19104 United States SKL of Computer Architecture Institute of Computing Technology CAS Beijing100190 China Department of Electrical and Computer Engineering Northeastern University Boston02115 United States

DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with non-robust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead. © 2022, CC BY.

关键词： Object detection

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UserReg: A simple but strong model for rating prediction

arXiv

引用

arXiv 2021年

作者： Zhang, Haiyang Ganchev, Ivan Nikolov, Nikola S. Stevenson, Mark Department of Computer Science The University of Sheffield United Kingdom Department of Computer Systems University of Plovdiv "Paisii Hilendarski" Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Bulgaria TRC University of Limerick Ireland CSIS University of Limerick Ireland

Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate their performance in comparison to baselines or existing models in terms of accuracy improvements. However, others have pointed out that many newly proposed models are not as strong as expected and are outperformed by very simple baselines. This paper proposes a simple linear model based on Matrix Factorization (MF), called UserReg, which regularizes users’ latent representations with explicit feedback information for rating prediction. We compare the effectiveness of UserReg with three linear CF models that are widely-used as baselines, and with a set of recently proposed complex models that are based on deep learning or graph techniques. Experimental results show that UserReg achieves overall better performance than the fine-tuned baselines considered and is highly competitive when compared with other recently proposed models. We conclude that UserReg can be used as a strong baseline for future CF research. © 2021, CC0.

关键词： Collaborative filtering

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A neuro-genetic Hopfield associative memory (HAM) with improved noise immunity

A neuro-genetic Hopfield associative memory (HAM) with impro...

引用

2020 National Conference on Functional Materials: Emerging Technologies and Applications in Materials science, NCFM 2020

作者： Varshney, Sudeep Singh, T.P. Singh, Amit Rajput, Pushpendra K. Singhal, Amit Varshney, Gunjan Sharda University Greater Noida India Deaprtment of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India Deaprtment of Cybernetics School of Computer Science University of Petroleum and Energy Studies Dehradun India Raj Kumar Goel Institute of Technology Ghaziabad India JSS Academy of Technical Education Noida India

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph. © 2021 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithms

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Industry 4.0 readiness in Hungary: Model, and the first results in connection to data application

引用

IFAC-PapersOnLine 2019年第13期52卷 289-294页

作者： Nick, Gábor Szaller, Ádám Bergmann, Júlia Várgedo, Tamás Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Hungary is one of the most industrialized countries in Europe, since many leading car companies together with their suppliers and related industries have production sites in this area. In the paper, a comprehensive questionnaire with 99 questions is introduced in detail, which aims to assess the Industry 4.0 readiness of companies in Hungary - taking into consideration some general information about the participating organization, the relevant company level Industry 4.0 maturity topics on the one side and, also issues related to national economy policy on the other side. The first results are presented here: the focus is on questions related to data collection, processing and utilization, in terms of the manufacturing processes, machines and products, as well. Based on the answers given by Hungarian organizations, it can be stated that most of them collect data about their production, but in general, the use of this data has not yet been integrated into the manufacturing and production processes. © 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automotive industry Data acquisition Data handling Industry 4.0 Manufacturing data processing Digitalization General information Industrialized countries Manufacturing process Maturity model National economy Production process Readiness assessment

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Bounding the Rate of Convergence for One Class of Finite Capacity Time Varying Markov Queues 1

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16th European Workshop on computer Performance Engineering, EPEW 2019

作者： Zeifman, Alexander Satin, Yacov Razumchik, Rostislav Kryukova, Anastasia Shilova, Galina Vologda State University Institute of Informatics Problems Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vologda Research Center of RAS Vologda Russia Vologda State University Vologda Russia Moscow Russia

ISBN: (数字)9783030444112

ISBN: (纸本)9783030444105

Consideration is given to the two finite capacity time varying Markov queues: the analogue of the well-known time varying M/M/S/0 queue with S servers each working at rate, no waiting line, but with the arrivals happening at rate only in batches of size 2;the analogue of another well-known time varying queue, but with the server, providing service at rate if and only if there are at least 2 customers in the system, and with the arrivals happening only in batches of size 2. The functions and are assumed to be non-random non-negative analytic functions of t. The new approach for the computation of the upper bound for the rate of convergence is proposed. It is based on the differential inequalities for the reduced forward Kolmogorov system of differential equations. Feasibility of the approach is demonstrated by the numerical example. © 2020, Springer Nature Switzerland AG.

关键词： Computational complexity

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Design of ensemble classifier using Statistical Gradient and Dynamic Weight LogitBoost for malicious tumor detection

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Journal of Ambient Intelligence and Humanized Computing 2020年第6期12卷 6713-6723页

作者： Subash Chandra Bose, S. Sivanandam, Natarajan Praveen Sundar, P. V. Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Department of Computer Science School of Informatics Wolaita Soda University Wolaita Soda Addis Ababa Ethiopia Department of Computer Science Adhiparasakthi College of Arts and Science Ranipet India

In medical field, the detection of abnormalities in breast is essential to find earlier stage of breast tumor. Conventional semi-supervised ensemble framework based on the normalized cut algorithm developed and it strongly improves the detection accuracy of the resulting images. However, further development of classification algorithm with minimum computational time and cost, several conventional methods limits the classification of tumor. In this paper, Statistical Gradient and Dynamic Weight based LogitBoost (SG-DWL) Ensemble approach is presented to improve the detection rate of malicious tumor. The key objective of SG-DWL Ensemble approach is to increase the malicious tumor performance with higher accuracy and lesser time consumption. The Statistical Gradient Boosting model is introduced as a numerical technique to improve feature set identification in test images. The proposed feature ensemble is formed by concatenating the probability density function, gradient vector and powerful algorithmic framework for feature selection with the best fit feature set is selected. Dynamic Weight based LogitBoost classifier (DW-LC) is applied for malicious tumor detection. This Dynamic Weight based LogitBoost classifier uses Hoeffding tree to achieve high malicious tumor detection rate by reducing the computational complexity involved in the classification of benign and malignant tumor. The performance of the proposed approach is evaluated by comparing it with the existing approaches, and the results improve the classification accuracy with minimum time period for malicious tumor detection.

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