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arXiv 2022年

作者： Kubota, Tomoyuki Suzuki, Yudai Kobayashi, Shumpei Tran, Quoc Hoan Yamamoto, Naoki Nakajima, Kohei Graduate School of Information Science and Technology The University of Tokyo Japan Department of Mechanical Engineering Keio University Japan Department of Creative Informatics The University of Tokyo Japan Quantum Computing Center Keio University Japan Department of Applied Physics and Physico-Informatics Keio University Japan Department of Mechano-Informatics The University of Tokyo Japan

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We tackle this issue by proposing a framework called quantum noise-induced reservoir computing and show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework’s processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices’ higher noise levels and error rates. Our study opens up a novel path for diverting useful information from quantum computer noises into a more sophisticated information processor. © 2022, CC BY.

关键词： Quantum noise

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kuan-Cheng Chen Yi-Tien Li Tai-Yu Li Chen-Yu Liu Po-Heng Henry Lee Cheng-Yu Chen Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan National Center for High-Performance Computing NARlabs Hsinchu Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Medical Imaging Taipei Medical University Hospital Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Taiwan

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

关键词： Neuroimaging Support vector machines Quantum computing Accuracy Magnetic resonance imaging Pipelines Machine learning Medical services Signal processing Standards

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Clairvoyance: Intelligent route planning for electric buses based on urban big data

arXiv

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arXiv 2021年

作者： Lu, Xiangyong Ota, Kaoru Dong, Mianxiong Yu, Chen Jin, Hai Graduate School of Information Sciences Tohoku University Sendai Japan Serviecs Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Information and Electronic Engineering Muroran Institute of Technology Hokkaido Muroran Japan

Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce the local carbon emissions. In order to cut the carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people’s trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people’s trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on the real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Generalization error bound for hyperbolic ordinal embedding

arXiv

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arXiv 2021年

作者： Suzuki, Atsushi Nitanda, Atsushi Wang, Jing Xu, Linchuan Cavazza, Marc Yamanishi, Kenji School of Computing and Mathematical Sciences Faculty of Liberal Arts and Sciences University of Greenwich United Kingdom Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Department of Computing Hong Kong Polytechnic University Hong Kong Department of Mathematical Informatics Graduate School of Information Science and Technology University of Tokyo Japan

Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the formof entity i ismore similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property. However, its theoretical analysis has been limited to ideal noiseless settings, and its generalization error in compensation for hyperbolic space's exponential representation ability has not been guaranteed. The difficulty is that existing generalization error bound derivations for ordinal embedding based on the Gramian matrix do not work in HOE, since hyperbolic space is not inner-product space. In this paper, through our novel characterization of HOE with decomposed Lorentz Gramian matrices, we provide a generalization error bound of HOE for the first time, which is at most exponential with respect to the embedding space's radius. Our comparison between the bounds of HOE and Euclidean ordinal embedding shows that HOE's generalization error is reasonable as a cost for its exponential representation ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

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Continuum percolation and stochastic epidemic models on poisson and ginibre point processes

arXiv

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arXiv 2021年

作者： Katori, Machiko Katori, Makoto Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Physics Faculty of Science and Engineering Chuo University Kasuga Bunkyo-ku Tokyo112-8551 Japan

The most studied continuum percolation model in two dimensions is the Boolean model consisting of disks with the same radius whose centers are randomly distributed on the Poisson point process (PPP). We also consider the Boolean percolation model on the Ginibre point process (GPP) which is a typical repelling point process realizing hyperuniformity. We think that the PPP approximates a disordered configuration of individuals, while the GPP does a configuration of citizens adopting a strategy to keep social distancing in a city in order to avoid contagion. We consider the SIR models with contagious infection on supercritical percolation clusters formed on the PPP and the GPP. By numerical simulations, we studied dependence of the percolation phenomena and the infection processes on the PPP- and the GPP-underlying graphs. We show that in a subcritical regime of infection rate the PPP-based models show emergence of infection clusters on clumping of points which is formed by fluctuation of uncorrelated Poissonian statistics. On the other hand, the cumulative numbers of infected individuals in processes are suppressed in the GPP-based models. © 2021, CC BY.

关键词： Solvents

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Fermionic quantum approximate optimization algorithm

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Physical Review Research 2023年第2期5卷 023071-023071页

作者： Takuya Yoshioka Keita Sasada Yuichiro Nakano Keisuke Fujii Strategic Technology Center TIS Inc. 2-2-1 Toyosu Koto-ku Tokyo 135-0061 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka Univrsity 1-2 Machikaneyama Toyonaka Osaka 560-0043 Japan Center for Quantum Computing RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan

Quantum computers are expected to accelerate solving combinatorial optimization problems, including algorithms such as Grover adaptive search and quantum approximate optimization algorithm (QAOA). However, many combinatorial optimization problems involve constraints which, when imposed as soft constraints in the cost function, can negatively impact the performance of the optimization algorithm. In this paper, we propose fermionic quantum approximate optimization algorithm (FQAOA) for solving combinatorial optimization problems with constraints. Specifically, FQAOA tackles the constrains issue by using fermion particle number preservation to intrinsically impose them throughout QAOA. We provide a systematic guideline for designing the driver Hamiltonian for a given problem Hamiltonian with constraints. The initial state can be chosen to be a superposition of states satisfying the constraint and the ground state of the driver Hamiltonian. This property is important since FQAOA reduced to quantum adiabatic computation in the large limit of circuit depth p and improved performance, even for shallow circuits with optimizing the parameters starting from the fixed-angle determined by Trotterized quantum adiabatic evolution. We perform an extensive numerical simulation and demonstrate that proposed FQAOA provides substantial performance advantages against existing approaches in portfolio optimization problems. Furthermore, the Hamiltonian design guideline is useful not only for QAOA but also Grover adaptive search and quantum phase estimation to solve combinatorial optimization problems with constraints. Since software tools for fermionic systems have been developed in quantum computational chemistry both for noisy intermediate-scale quantum computers and fault-tolerant quantum computers, FQAOA allows us to apply these tools for constrained combinatorial optimization problems.

关键词： Quantum algorithms Quantum computation Quantum simulation Quantum theory

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Experimental computations of atomic properties on a superconducting quantum processor

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Physical Review A 2024年第6期110卷 062620-062620页

作者： Akhil Pratap Singh Kenji Sugisaki Srinivasa Prasannaa Bijaya Kumar Sahoo Bhanu Pratap Das Yasunobu Nakamura Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan Present address: QCD Labs QTF Centre of Excellence Department of Applied Physics Aalto University P. O. Box 13500 FIN-00076 Aalto Finland. Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki Kanagawa 212-0032 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC) Keio University 2-15-45 Mita Minato-ku Tokyo 108-8345 Japan Centre for Quantum Engineering Research and Education TCG CREST Sector V Salt Lake Kolkata 700091 India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad 201002 India Atomic Molecular and Optical Physics Division Physical Research Laboratory Navrangpura Ahmedabad 380009 India Department of Physics Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

We experimentally compute relativistic and correlation effects in the atomic properties by using a superconducting qubit processor. Specifically, we compute the relativistic ground-state energies and magnetic-dipole hyperfine structure constants for four Li-like atomic systems ranging from very light to moderately heavy to very heavy in terms of nuclear charge. A symmetry-conserving Bravyi-Kitaev transformation is used to reduce the original six-qubit problem to a four-qubit problem, which is experimentally contrived by reducing the hardware requirement by employing a virtual two-qubit gate. It enables the simulation of four-qubit circuits using two physical qubits with additional circuit evaluations. The ground-state wave functions, required for computing atomic properties, are obtained by using quantum state tomography. Our results show that the averaged relative errors for the ground-state energies are ≈0.3±1%. However, for the hyperfine structure constants, the mean values of the relative errors are less than 15%, with their estimated upper bound of relative errors of ≈±10% (with the exception of 7.2±47% for neutral Li7). Notably, our results for the hyperfine structure constants exhibit higher sensitivity to errors as compared to energies; a trend which we also confirm through additional numerical simulations.

关键词： Qubits

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Categorizing learning analytics models according to their goals and identifying their relevant components: A review of the learning analytics literature from 2011 to 2019

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Computers and Education: Artificial Intelligence 2021年 2卷

作者： Quadir, Benazir Chang, Maiga Yang, Jie Chi Information Management Department School of Business. Shandong University of Technology China School of Computing and Information Systems Athabasca University Canada Graduate Institute of Network Learning Technology. &. Department of Computer Science and Information Engineering National Central University Taiwan

This study aimed to categorize learning analytics (LA) models and identify their relevant components by analyzing LA-related articles published between 2011 and 2019 in international journals. A total of 101 articles discussing various LA models were selected. These models were characterized according to their goals and components. A qualitative content analysis approach was used to develop a coding scheme for analyzing the aforementioned models. The results reveal that the studied LA models belong to five categories, namely performance, meta-cognitive, interactivity, communication, and data models. The majority of the selected LA-related articles were data models, followed by performance models. This review also identified 16 components that were commonly used in the studied models. The results indicate that analytics was the most common component in the studied models (used in 10 LA models). Furthermore, visualization was the most relevant component in the studied communication models. © 2021 The Authors

关键词： Learning systems

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

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Research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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