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arXiv 2020年

作者： Huang, Cupjin Zhang, Fang Newman, Michael Cai, Junjie Gao, Xun Tian, Zhengxiong Wu, Junyin Xu, Haihong Yu, Huanjun Yuan, Bo Szegedy, Mario Shi, Yaoyun Chen, Jianxin Alibaba Quantum Laboratory Alibaba Group USA BellevueWA98004 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Departments of Physics and Electrical and Computer Engineering Duke University DurhamNC27708 United States Alibaba Cloud Intelligence Alibaba Group USA BellevueWA98004 United States Alibaba Cloud Intelligence Alibaba Group HangzhouZhejiang310000 China Alibaba Infrastructure Service Alibaba Group HangzhouZhejiang310000 China

It is believed that random quantum circuits are difficult to simulate classically. These have been used to demonstrate quantum supremacy: the execution of a computational task on a quantum computer that is infeasible for any classical computer. The task underlying the assertion of quantum supremacy by Arute et al. (Nature, 574, 505–510 (2019)) was initially estimated to require Summit, the world’s most powerful supercomputer today, approximately 10,000 years. The same task was performed on the Sycamore quantum processor in only 200 seconds. In this work, we present a tensor network-based classical simulation algorithm. Using a Summit-comparable cluster, we estimate that our simulator can perform this task in less than 20 days. On moderately-sized instances, we reduce the runtime from years to minutes, running several times faster than Sycamore itself. These estimates are based on explicit simulations of parallel subtasks, and leave no room for hidden costs. The simulator’s key ingredient is identifying and optimizing the "stem" of the computation: a sequence of pairwise tensor contractions that dominates the computational cost. This orders-of-magnitude reduction in classical simulation time, together with proposals for further significant improvements, indicates that achieving quantum supremacy may require a period of continuing quantum hardware developments without an unequivocal first demonstration. Copyright © 2020, The Authors. All rights reserved.

关键词： Supercomputers

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Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks

arXiv

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arXiv 2019年

作者： Wen, Bihan Ravishankar, Saiprasad Pfister, Luke Bresler, Yoram School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Departments of Computational Mathematics Science and Engineering and Biomedical Engineering Michigan State University East LansingMI48824 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-ChampaignIL61801 United States

Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in known analytical dictionaries or transform domains, methods using learned image models for reconstruction have become popular. The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i.e., blind CS (BCS). Besides the well-known synthesis dictionary model, recent advances in transform learning (TL) provide an efficient alternative framework for sparse modeling in MRI. TL-based methods enjoy numerous advantages including exact sparse coding, transform update, and clustering solutions, cheap computation, and convergence guarantees, and provide high-quality results in MRI compared to popular competing methods. This paper provides a review of some recent works in MRI reconstruction from limited data, with focus on the recent TL-based methods. A unified framework for incorporating various TL-based models is presented. We discuss the connections between transform learning and convolutional or filterbank models and corresponding multilayer extensions, with connections to deep learning. Finally, we discuss recent trends in MRI, open problems, and future directions for the field. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

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Modeling network populations via graph distances ∗

arXiv

引用

arXiv 2019年

作者： Lunagómez, Simón Olhede, Sofia C. Wolfe, Patrick J. Department of Mathematics and Statistics Lancaster University Institute of Mathematics Ecole polytechnique fédérale de Lausanne Departments of Statistics Computer Science and Electrical & Computer Engineering Purdue University

This article introduces a new class of models for multiple networks. The core idea is to parametrize a distribution on labelled graphs in terms of a Fréchet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter that controls the concentration of this distribution about its mean. Entropy is the natural parameter for such control, varying from a point mass concentrated on the Fréchet mean itself to a uniform distribution over all graphs on a given vertex set. We provide a hierarchical Bayesian approach for exploiting this construction, along with straightforward strategies for sampling from the resultant posterior distribution. We conclude by demonstrating the efficacy of our approach via simulation studies and two multiple-network data analysis examples: one drawn from systems biology and the other from neuroscience. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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Leakage mitigation for quantum error correction using a mixed qubit scheme

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Physical Review A 2019年第3期100卷 032325-032325页

作者： Natalie C. Brown Kenneth R. Brown School of Physics Georgia Institute of Technology Atlanta Georgia 30332 USA Schools of Chemistry and Biochemistry and Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Departments of Electrical and Computer Engineering Chemistry and Physics Duke University Durham North Carolina 27708 USA

Leakage errors take qubits out of the computational subspace and will accumulate if not addressed. A leaked qubit will reduce the effectiveness of quantum error correction protocols due to the cost of implementing leakage reduction circuits and the harm caused by interacting leaked states with qubit states. Ion trap qubits driven by Raman gates have a natural choice between qubits encoded in magnetically insensitive hyperfine states that can leak and qubits encoded in magnetically sensitive Zeeman states of the electron spin that cannot leak. In our previous work, we compared these two qubits in the context of the toric code with a depolarizing leakage error model and found that for magnetic-field noise with a standard deviation less than 32 μG that the Yb+174 Zeeman qubit outperforms the Yb+171 hyperfine qubit. Here we examine a physically motivated leakage error model based on ions interacting via the Mølmer-Sørenson gate. We find that this greatly improves the performance of hyperfine qubits but the Zeeman qubits are more effective for magnetic-field noise with a standard deviation less than 10 μG. At these low magnetic fields, we find that the best choice is a mixed qubit scheme where the hyperfine qubits are the syndrome qubits and the leakage is handled without the need of an additional leakage reduction circuit.

关键词： Quantum error correction Quantum information processing Quantum memories Surface code quantum computing Trapped ions

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Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles

Duckiepond: An Open Education and Research Environment for a...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ni-Ching Lin Yu-Chieh Hsiao Yi-Wei Huang Ching-Tung Hung Tzu-Kuan Chuang Pin-Wei Chen Jui-Te Huang Chao-Chun Hsu Andrea Censi Michael Benjamin Chi-Fang Chen Hsueh-Cheng Wang Department of Electrical and Computer Engineering National Chiao Tung University Taiwan Department of Engineering Science and Ocean Engineering National Taiwan University Taiwan Department of Mechanical and Process Engineering ETH Zürich Switzerland Department of Mechanical Engineering Massachusetts Institute of Technology USA

Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duckieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, with flexible software that leverages several open source packages. The Duckiepond environment is modeled after Duckietown and AI Driving Olympics environments: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. Duckiepond coordinates commonly used middlewares (ROS and MOOS) and containerized software packages in Docker, making it easy to deploy. The combination of learning-based methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duckieboats to avoid collisions. Duckieboats have been operating in a man-made lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https://***/duckiepond), with the goal of supporting research and education communities across related domains.

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Understanding Continuous and Pleasant Linear Sensations on the Forearm from a Sequential Discrete Lateral Skin-Slip Haptic Device

arXiv

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arXiv 2019年

作者： Nunez, Cara M. Williams, Sophia R. Okamura, Allison M. Culbertson, Heather Departments of Bioengineering Electrical Engineering and Mechanical Engineering Stanford University StanfordCA94305 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States

A continuous stroking sensation on the skin can convey messages or emotion cues. We seek to induce this sensation using a combination of illusory motion and lateral stroking via a haptic device. Our system provides discrete lateral skin-slip on the forearm with rotating tactors, which independently provide lateral skin-slip in a timed sequence. We vary the sensation by changing the angular velocity and delay between adjacent tactors, such that the apparent speed of the perceived stroke ranges from 2.5 to 48.2 cm/s. We investigated which actuation parameters create the most pleasant and continuous sensations through a user study with 16 participants. On average, the sensations were rated by participants as both continuous and pleasant. The most continuous and pleasant sensations were created by apparent speeds of 7.7 and 5.1 cm/s, respectively. We also investigated the effect of spacing between contact points on the pleasantness and continuity of the stroking sensation, and found that the users experience a pleasant and continuous linear sensation even when the space between contact points is relatively large (40 mm). Understanding how sequential discrete lateral skin-slip creates continuous linear sensations can influence the design and control of future wearable haptic devices. Copyright © 2019, The Authors. All rights reserved.

关键词： Feedback

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Emergent Prethermalization Signatures in Out-of-Time Ordered Correlations

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Physical Review Letters 2019年第9期123卷 090605-090605页

作者： Ken Xuan Wei Pai Peng (彭湃) Oles Shtanko Iman Marvian Seth Lloyd Chandrasekhar Ramanathan Paola Cappellaro Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Departments of Physics & Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy Dartmouth College Hanover New Hampshire 03755 USA Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

How a many-body quantum system thermalizes—or fails to do so—under its own interaction is a fundamental yet elusive concept. Here we demonstrate nuclear magnetic resonance observation of the emergence of prethermalization by measuring out-of-time ordered correlations. We exploit Hamiltonian engineering techniques to tune the strength of spin-spin interactions and of a transverse magnetic field in a spin chain system, as well as to invert the Hamiltonian sign to reveal out-of-time ordered correlations. At large fields, we observe an emergent conserved quantity due to prethermalization, which can be revealed by an early saturation of correlations. Our experiment not only demonstrates a new protocol to measure out-of-time ordered correlations, but also provides new insights in the study of quantum thermodynamics.

关键词： Eigenstate thermalization Quantum simulation Quantum thermodynamics 1-dimensional spin chains Magnetic moment Nuclear magnetic resonance

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Unaligned sequence similarity search using deep learning

arXiv

引用

arXiv 2019年

作者： Senter, James K. Royalty, Taylor M. Steen, Andrew D. Sadovnik, Amir Department of Electrical Engineering and Computer Science University of Tennessee - Knoxville KnoxvilleTN Departments of Microbiology and Earth and Planetary Sciences University of Tennessee - Knoxville KnoxvilleTN

Gene annotation has traditionally required direct comparison of DNA sequences between an unknown gene and a database of known ones using string comparison methods. However, these methods do not provide useful information when a gene does not have a close match in the database. In addition, each comparison can be costly when the database is large since it requires alignments and a series of string comparisons. In this work we propose a novel approach: using recurrent neural networks to embed DNA or amino-acid sequences in a low-dimensional space in which distances correlate with functional similarity. This embedding space overcomes both shortcomings of the method of aligning sequences and comparing homology. First, it allows us to obtain information about genes which do not have exact matches by measuring their similarity to other ones in the database. If our database is labeled this can provide labels for a query gene as is done in traditional methods. However, even if the database is unlabeled it allows us to find clusters and infer some characteristics of the gene population. In addition, each comparison is much faster than traditional methods since the distance metric is reduced to the Euclidean distance, and thus efficient approximate nearest neighbor algorithms can be used to find the best match. We present results showing the advantage of our algorithm. More specifically we show how our embedding can be useful for both classification tasks when our labels are known, and clustering tasks where our sequences belong to classes which have not been seen before. Copyright © 2019, The Authors. All rights reserved.

关键词： Genes

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Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

arXiv

引用

arXiv 2019年

作者： Ravishankar, Saiprasad Chul Ye, Jong Fessler, Jeffrey A. Departments of Computational Mathematics Science and Engineering and Biomedical Engineering Michigan State University East LansingMI48824 United States Daejeon Korea Republic of Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

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Energy efficiency maximization in ris-aided cell-free network with limited backhaul

arXiv

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arXiv 2020年

作者： Le, Quang Nhat Nguyen, Van-Dinh Dobre, Octavia A. Zhao, Ruiqin Department of Electrical and Computer Engineering Memorial University St. John'sNLA1B 3X9 Canada University of Luxembourg L-1855 Luxembourg Key Laboratory of Ocean Acoustics and Sensing School of Marine Science and Technology Northwestern Polytechnical University Xi'an710072 China

Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption. The reflecting coefficients of RISs can be programmed to enhance signals received at users. This letter addresses a joint design of transmit beamformers at access points and reflecting coefficients at RISs to maximize the energy efficiency (EE) of RIS-CF networks, taking into account the limited backhaul capacity constraints. Due to a very computationally challenging nonconvex problem, we develop a simple yet efficient alternating descent algorithm for its solution. Numerical results verify that the EE of RIS-CF networks is greatly improved, showing the benefit of using RISs. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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