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Quantum supremacy in driven quantum many-body systems

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arXiv 2020年

作者： Tangpanitanon, Jirawat Thanasilp, Supanut Lemonde, Marc-Antoine Dangniam, Ninnat Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece

A crucial milestone in the field of quantum simulation and computation is to demonstrate that a quantum device can perform a computation task that is classically intractable. A key question is to identify setups that can achieve such goal within current technologies. Here, we provide formal evidence that a large class of driven analog quantum systems constitute practical candidates for quantum supremacy. Our analysis is based on the Floquet eigenstate thermalization hypothesis, plausible assumptions and the absence of collapse of the polynomial hierarchy. We give examples of driven disordered Ising chains and 1D driven Bose-Hubbard model. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

Spectrum Manipulation for Sound with Effective Gauge Fields in Cascading Temporally Modulated Waveguides

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Physical Review Applied 2019年第6期11卷 064012-064012页

作者： Chengzhi Qin Yugui Peng Ying Li Xuefeng Zhu Bing Wang Cheng-Wei Qiu Peixiang Lu School of Physics and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 119620 Singapore Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan 430205 China

Spectrum is a basic dimension of waves ranging from electromagnetic to acoustic waves where information can be encoded and multiplexed. The manipulation of the sound spectrum is desirable in applications of acoustic communication and voice encryption, which, however, is challenging to realize. Here, based on temporally modulated waveguides, we create effective gauge fields to generate frequency domain Bloch oscillations (FBOs) to control the spectrum of sound. The modulation can induce mode transitions in the waveguide band and form a discrete frequency lattice where the wave vector mismatch during transitions acts as an effective electric field that drives FBOs. Furthermore, we find the modulation phase accompanying transitions serves as an effective gauge potential that can control the initial oscillation phase. We report that multiple FBOs with judiciously designed oscillation phases can be further cascaded to realize acoustic spectrum reconstruction, unidirectional transduction, and bandwidth engineering. This study reveals the significance of gauge fields in FBOs and functionalizes its cascaded configurations for advanced control of the sound spectrum. This paradigm may find versatile applications in acoustic secure communication, information encryption, and processing.

关键词： Acoustic signal processing & noise Acoustic wave phenomena Elastic modulus Geometric & topological phases Synthetic gauge fields Waveguides Bloch wave theory

Framework for Artificial Intelligence Analysis in Large-scale Power Grids Based on Digital Simulation

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CSEE Journal of Power and Energy Systems 2018年第4期4卷 459-468页

作者： Yong Tang Yanhao Huang Hongzhi Wang Can Wang Qiang Guo Wei Yao State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China College of Computer Science at Harbin Institute of Technology Harbin 150001China College of Computer Science at Zhejiang University Hangzhou 310027China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China

The planning,design,operation,control and scientific research of power systems all require a variety of simulation *** power grid simulation analysis is a fundamental supporting technology of large-scale power *** power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on *** introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation ***,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex *** a great deal of knowledge and experience need to be integrated in the process of *** order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,*** work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.

关键词： Artificial intelligence digital simulation power system analysis

Expressibility and trainability of parameterized analog quantum systems for machine learning applications

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arXiv 2020年

作者： Tangpanitanon, Jirawat Thanasilp, Supanut Dangniam, Ninnat Lemonde, Marc-Antoine Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece

Parameterized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices. In digital quantum computing, it has been shown that random parameterized quantum circuits are able to express complex distributions intractable by a classical computer, leading to the demonstration of quantum supremacy. However, their chaotic nature makes parameter optimization challenging in variational approaches. Evidence of similar classically-intractable expressibility has been recently demonstrated in analog quantum computing with driven many-body systems. A thorough investigation of trainability of such analog systems is yet to be performed. In this work, we investigate how the interplay between external driving and disorder in the system dictates the trainability and expressibility of interacting quantum systems. We show that if the system thermalizes, the training fails at the expense of the a large expressibility, while the opposite happens when the system enters the many-body localized (MBL) phase. From this observation, we devise a protocol using quenched MBL dynamics which allows accurate trainability while keeping the overall dynamics in the quantum supremacy regime. Our work shows the fundamental connection between quantum many-body physics and its application in machine learning. We conclude our work with an example application in generative modeling employing a well studied analog many-body model of a driven Ising spin chain. Our approach can be implemented with a variety of available quantum platforms including cold ions, atoms and superconducting circuits. Copyright © 2020, The Authors. All rights reserved.

关键词： Parameterization

Overview of digital health surveillance system during COVID-19 pandemic: Public health issues and misapprehensions

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arXiv 2020年

作者： Hussein, Molla Rashied Apu, Ehsanul Hoque Shahabuddin, Shahriar Shams, Abdullah Bin Kabir, Russell Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Department of Biomedical Engineering Michigan State University East LansingMI48824 United States Laboratory of Developmental Biology Faculty of Biochemistry and Molecular Medicine University of Oulu Aapistie 5A Oulu90220 Finland Mobile Networks Nokia Oulu Finland Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada School of Allied Health Faculty of Health Education Medicine and Social Care Anglia Ruskin University Chelmsford Essex United Kingdom

Table Structure Extraction with Bi-Directional Gated Recurrent Unit Networks

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Table Structure Extraction with Bi-Directional Gated Recurre...

International Conference on Document Analysis and Recognition

作者： Saqib Ali Khan Syed Muhammad Daniyal Khalid Muhammad Ali Shahzad Faisal Shafait School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Islamabad Pakistan

Tables present summarized and structured information to the reader, which makes table's structure extraction an important part of document understanding applications. However, table structure identification is a hard problem not only because of the large variation in the table layouts and styles, but also owing to the variations in the page layouts and the noise contamination levels. A lot of research has been done to identify table structure, most of which is based on applying heuristics with the aid of optical character recognition (OCR) to hand pick layout features of the tables. These methods fail to generalize well because of the variations in the table layouts and the errors generated by OCR. In this paper, we have proposed a robust deep learning based approach to extract rows and columns from a detected table in document images with a high precision. In the proposed solution, the table images are first pre-processed and then fed to a bi-directional Recurrent Neural Network with Gated Recurrent Units (GRU) followed by a fully-connected layer with softmax activation. The network scans the images from top-to-bottom as well as left-to-right and classifies each input as either a row-separator or a column-separator. We have benchmarked our system on publicly available UNLV as well as ICDAR 2013 datasets on which it outperformed the state-of-theart table structure extraction systems by a significant margin.

关键词： Layout Bidirectional control Image segmentation Deep learning Recurrent neural networks Logic gates Optical character recognition software

Fast graph sampling set selection using gershgorin disc alignment

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arXiv 2019年

作者： Bai, Yuanchao Wang, Fen Cheung, Gene Nakatsukasa, Yuji Gao, Wen School of Electronics Engineering and Computer Science Peking University Beijing100871 China Department of Electrical Engineering & Computer Science York University Toronto Canada State Key Laboratory of ISN Xidian University Xi'an Shaanxi710071 China University of Oxford United Kingdom National Institute of Informatics Japan

Graph sampling set selection, where a subset of nodes are chosen to collect samples to reconstruct a bandlimited or smooth graph signal, is a fundamental problem in graph signal processing (GSP). Previous works employ an unbiased least square (LS) signal reconstruction scheme and select samples via expensive extreme eigenvector computation. Instead, we assume a biased graph Laplacian regularization (GLR) based scheme that solves a system of linear equations for reconstruction. We then choose samples to minimize the condition number of the coefficient matrix-specifically, maximize the smallest eigenvalue λmin. Circumventing explicit eigenvalue computation, we maximize instead the lower bound of λmin, designated by the smallest left-end of all Gershgorin discs of the matrix. To achieve this efficiently, we first convert the optimization to a dual problem, where we minimize the number of samples needed to align all Gershgorin disc left-ends at a chosen lower-bound target T. Algebraically, the dual problem amounts to optimizing two disc operations: i) shifting of disc centers due to sampling, and ii) scaling of disc radii due to a similarity transformation of the matrix. We further reinterpret the dual to an intuitive disc coverage problem bearing strong resemblance to the famous NP-hard set cover (SC) problem. The reinterpretation enables us to derive a fast approximation algorithm from a known SC error-bounded approximation algorithm. We find the appropriate target T efficiently via binary search. Experiments on real graph data show that our disc-based sampling algorithm runs substantially faster than existing sampling schemes and outperforms other eigen-decomposition-free sampling schemes in reconstruction error. Copyright © 2019, The Authors. All rights reserved.

关键词： Approximation algorithms

Attitude Observation for Second Order Attitude Kinematics

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Attitude Observation for Second Order Attitude Kinematics

IEEE Annual Conference on Decision and Control

作者： Yonhon Ng Pieter van Goor Robert Mahony Tarek Hamel Research School of Electrical Energy and Materials Engineering Australian National University Acton ACT Australia Computer Science Signals and Systems Laboratory University of Nice Sophia Antipolis Nice France

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper addresses the problem of estimating the attitude and angular velocity of a rigid object by exploiting its second order kinematic model. The approach is particularly useful in cases where angular velocity measurements are not available and the attitude and angular velocity of an object need to be estimated from accelerometers and magnetometers. We propose a novel sensor modality that uses multiple accelerometers to measure the angular acceleration of an object as well as using magnetometers to measure partial attitude. We extend the approach of equivariant observer design to second order attitude kinematics by demonstrating that the special Euclidean group acts as a symmetry group on the system considered. The observer design is based on the lifted kinematics and we prove almost global asymptotic stability and local uniform exponential stability of the estimation error. The performance of the observer is demonstrated in simulation.

关键词： Kinematics Angular velocity Accelerometers Observers Magnetometers Acceleration Velocity measurement

Rethinking Table Recognition using Graph Neural Networks

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Rethinking Table Recognition using Graph Neural Networks

International Conference on Document Analysis and Recognition

作者： Shah Rukh Qasim Hassan Mahmood Faisal Shafait School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Islamabad Pakistan

Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since conventional neural networks are not well suited to the input structure of the problem. In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition. We argue that graph networks are a more natural choice for these problems, and explore two gradient-based graph neural networks. Our proposed architecture combines the benefits of convolutional neural networks for visual feature extraction and graph networks for dealing with the problem structure. We empirically demonstrate that our method outperforms the baseline by a significant margin. In addition, we identify the lack of large scale datasets as a major hindrance for deep learning research for structure analysis and present a new large scale synthetic dataset for the problem of table recognition. Finally, we open-source our implementation of dataset generation and the training framework of our graph networks to promote reproducible research in this direction.

关键词： Feature extraction computer architecture Convolutional neural networks Text analysis Deep learning Training