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Variational learning algorithms for quantum query complexity

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arXiv 2022年

作者： Wu, Zipeng Hou, Shi-Yao Zhang, Chao Li, Lvzhou Zeng, Bei Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Shenzhen SpinQ Technology Co. Ltd. Shenzhen China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Quantum query complexity is pivotal in the analysis of quantum algorithms, encompassing wellknown examples like search and period-finding algorithms. These algorithms typically involve a sequence of unitary operations and oracle calls dependent on an input variable. In this study, we introduce a variational learning approach to explore quantum query complexity. Our method employs an efficient parameterization of the unitary operations and utilizes a loss function derived from the algorithm's error probability. We apply this technique to various quantum query complexities, notably devising a new algorithm that resolves the 5-bit Hamming modulo problem with four queries, addressing an open question from arXiv:2112.14682. This finding is corroborated by a Semidefinite Programming (SDP) approach. Our numerical method exhibits superior memory efficiency compared to SDP and can identify quantum query algorithms that require a smaller workspace register dimension, an aspect not optimized by SDP. These advancements present a significant step forward in the practical application and understanding of quantum query algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

Active tactile object exploration with Gaussian processes

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Active tactile object exploration with Gaussian processes

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Zhengkun Yi Roberto Calandra Filipe Veiga Herke van Hoof Tucker Hermans Yilei Zhang Jan Peters Intelligent Autonomous Systems Lab Department of Computer Science Technische Universitat Darmstadt Hochschulstr. 10 64289 Germany School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 University of Utah Robotics Center School of Computing University of Utah USA

ISBN: (纸本)9781509037636

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.

关键词： Gaussian processes Touch tactile sensors grid point

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

A Survey on Computationally Efficient Neural Architecture Search

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arXiv 2022年

作者： Liu, Shiqing Zhang, Haoyu Jin, Yaochu Nature Inspired Computing and Engineering Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom The Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve this major limitation of NAS, improving the computational efficiency is essential in the design of NAS. However, a systematic overview of computationally efficient NAS (CE-NAS) methods still lacks. To fill this gap, we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods, together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities. The remaining challenges and open research questions are also discussed, and promising research topics in this emerging field are suggested. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Deep learning for Estimation and pilot signal design in few-bit massive MIMO systems

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arXiv 2021年

作者： Nguyen, Ly V. Nguyen, Duy H.N. Lee Swindlehurst, A. The Computational Science Research Center San Diego State University San DiegoCA92182 United States The Department of Electrical and Computer Engineering San Diego State University San DiegoCA92182 United States The Center for Pervasive Communications and Computing Henry Samueli School of Engineering University of California IrvineCA92697 United States

Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of the domain knowledge in the few-bit quantization process. While the first data detection network, namely B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, namely FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the vanishing gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into its weight matrices of the channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gain in estimation accuracy by the proposed deep learning framework. © 2021, CC BY.

关键词： MIMO systems

A language and hardware independent approach to quantum-classical computing

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arXiv 2017年

作者： McCaskey, A.J. Dumitrescu, E.F. Liakh, D. Chen, M. Feng, W. Humble, T.S. Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Oak Ridge Leadership Computing Facility Scientific Computing Oak Ridge National Laboratory Oak RidgeTN United States Department of Physics Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Bredesen Center for Interdisciplinary Research University of Tennessee KnoxvilleTN United States

Heterogeneous high-performance computing (HPC) systems offer novel architectures which accelerate specific workloads through judicious use of specialized coprocessors. A promising architectural approach for future scientific computations is provided by heterogeneous HPC systems integrating quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale ACCelerator) - a programming model and software framework that enables quantum acceleration within standard or HPC software workflows. XACC follows a coprocessor machine model that is independent of the underlying quantum computing hardware, thereby enabling quantum programs to be defined and executed on a variety of QPUs types through a unified application programming interface. Moreover, XACC defines a polymorphic low-level intermediate representation, and an extensible compiler frontend that enables language independent quantum programming, thus promoting integration and interoperability across the quantum programming landscape. In this work we define the software architecture enabling our hardware and language independent approach, and demonstrate its usefulness across a range of quantum computing models through illustrative examples involving the compilation and execution of gate and annealing-based quantum programs. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum computers

Metacognition for a Common Model of Cognition

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Procedia computer science 2018年 145卷 730-739页

作者： Jerald D. Kralik Jee Hang Lee Paul S. Rosenbloom Philip C. Jackson Susan L. Epstein Oscar J. Romero Ricardo Sanz Othalia Larue Hedda R. Schmidtke Sang Wan Lee Keith McGreggor Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 South Korea KI for Health Science and Technology Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 South Korea Institute for Creative Technologies & Department of Computer Science University of Southern California Los Angeles CA USA TalaMind LLC PMB #363 55 E. Long Lake Rd. Troy MI USA Department of Computer Science Hunter College and The Graduate Center of the City University of New York New York NY USA Department of Machine Learning Carnegie Mellon University Pittsburgh PA USA Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Department of Psychology Wright State University Dayton Ohio USA Department of Geography University of Oregon Eugene OR USA College of Computing Georgia Institute of Technology Atlanta Georgia USA

This paper provides a starting point for the development of metacognition in a common model of cognition. It identifies significant theoretical work on metacognition from multiple disciplines that the authors believe worthy of consideration. After first defining cognition and metacognition, we outline three general categories of metacognition, provide an initial list of its main components, consider the more difficult problem of consciousness, and present examples of prominent artificial systems that have implemented metacognitive components. Finally, we identify pressing design issues for the future.

关键词： Metacognition Common model Cognition Cognitive architecture

Bayesian Multivariate Sparse Functional Principal Components Analysis with application to longitudinal microbiome multi-omics data

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arXiv 2021年

作者： Jiang, Lingjing Elord, Chris Kim, Jane J. Swafford, Austin D. Knight, Rob Thompson, Wesley K. Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego United States Julia Computing Department of Pediatrics University of California San Diego United States Center for Microbiome Innovation University of California San Diego United States Department of Computer Science and Engineering University of California San Diego United States Department of Bioengineering University of California San Diego United States

Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate Sparse Functional Principal Components Analysis (mSFPCA), extending existing SFPCA methods to simultaneously characterize multiple temporal trajectories and their inter-relationships. As with existing SFPCA methods, the mSFPCA algorithm characterizes each trajectory as a smooth mean plus a weighted combination of the smooth major modes of variation about the mean, where the weights are given by the component scores for each subject. Unlike existing SFPCA methods, the mSFPCA algorithm allows estimation of multiple trajectories simultaneously, such that the component scores, which are constrained to be independent within a particular outcome for identifiability, may be arbitrarily correlated with component scores for other outcomes. A Cholesky decomposition is used to estimate the component score covariance matrix efficiently and guarantee positive semi-definiteness given these constraints. Mutual information is used to assess the strength of marginal and conditional temporal associations across outcome trajectories. Importantly, we implement mSFPCA as a Bayesian algorithm using R and stan, enabling easy use of packages such as PSIS-LOO for model selection and graphical posterior predictive checks to assess the validity of mSFPCA models. Although we focus on application of mSFPCA to microbiome data in this paper, the mSFPCA model is of general utility and can be used in a wide range of real-world applications. © 2021, CC BY.

关键词： Trajectories

When creators meet the metaverse: A survey on computational arts

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arXiv 2021年

作者： Lee, Lik-Hang Lin, Zijun Hu, Rui Gong, Zhengya Kumar, Abhishek Li, Tangyao Li, Sijia Hui, Pan Augmented Reality and Media Lab KAIST Korea Republic of Department of Economics University College London United Kingdom Computational Media & Arts The Hong Kong University of Science and Technology Hong Kong Center for Ubiquitous Computing University of Oulu Finland Department of Computer Science University of Helsinki Finland School of Mathematical Sciences Queen Mary University of London United Kingdom Department of Architecture Inha University Korea Republic of

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace. © 2021, CC BY-NC-SA.

关键词： Surveys