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arXiv 2023年

作者： Kumar, Anand Cholsuk, Chanaprom Mishuk, Mohammad N. Hazra, Mouli Pillot, Clotilde Matthes, Tjorben Shaik, Tanveer A. Çakan, Aslı Deckert, Volker Suwanna, Sujin Vogl, Tobias Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Abbe Center of Photonics Friedrich Schiller University Jena Jena07743 Germany Jena07745 Germany Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand

Quantum light sources are crucial foundational components for various quantum technology applications. With the rapid development of quantum technology, there has been a growing demand for materials with the capability of hosting quantum emitters. One such material platform uses fluorescent defects in hexagonal boron nitride (hBN) that can host deep sub-levels within the band gap. The localized electron irradiation has shown its effectiveness in generating deep sub-levels to induce single emitters in hBN. The question is whether localized (electron beam) irradiation is a reliable tool for creating emitters in other wide bandgap materials and its uniqueness to hBN. Here, we investigate and compare the fabrication of quantum emitters in hBN and exfoliated muscovite mica flakes along with other 3D crystals, such as silicon carbide and gallium nitride, which are known to host quantum emitters. We use our primary fabrication technique of localized electron irradiation using a standard scanning electron microscope. To complement our experimental work, we employ density functional theory simulations to study the atomic structures of defects in mica. While our fabrication technique allows to create hBN quantum emitters with a high yield and high single photon purity, it is unable to fabricate single emitters in the other solid-state crystals under investigation. This allows us to draw conclusions on the emitter fabrication mechanism in hBN, which could rely on activating pre-existing defects by charge state manipulation. Therefore, we provide an essential step towards the identification of hBN emitters and their formation process. © 2023, CC BY.

关键词： Silicon carbide

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Nonlinear Feedforward enabling Nonlinear Quadrature Measurement toward Fault-tolerant Universal Quantum computation

Nonlinear Feedforward enabling Nonlinear Quadrature Measurem...

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Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Sakguchi, Atsushi Konno, Shunya Hanamura, Fumiya Ogawa, Hisashi Takase, Kan Asavanant, Warit Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Yonezawa, Hidehiro Huntington, Elanor Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology Research School of Electrical Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia

We demonstrate nonlinear feedforward combined with non-Gaussian ancillae localized in time, which realizes nonlinear quadrature measurement. Our technology in principle enables fault-tolerant universal quantum computa... 详细信息

ISBN: (纸本)9781557528209

关键词： Quantum computers

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Overcoming entropic limitations on asymptotic state transformations through probabilistic protocols

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Physical Review A 2023年第4期107卷 042401-042401页

作者： Bartosz Regula Ludovico Lami Mark M. Wilde Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 D-89069 Ulm Germany QuSoft Science Park 123 1098 XG Amsterdam The Netherlands Korteweg-de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 1098 XG Amsterdam The Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands School of Electrical and Computer Engineering Cornell University Ithaca New York 14850 USA Hearne Institute for Theoretical Physics Department of Physics and Astronomy and Center for Computation and Technology Louisiana State University Baton Rouge Louisiana 70803 USA

The quantum relative entropy is known to play a key role in determining the asymptotic convertibility of quantum states in general resource-theoretic settings, often constituting the unique monotone that is relevant in the asymptotic regime. We show that this is no longer the case when one allows stochastic protocols that may only succeed with some probability, in which case the quantum relative entropy is insufficient to characterize the rates of asymptotic state transformations, and a new entropic quantity based on a regularization of the Hilbert projective metric comes into play. Such a scenario is motivated by a setting where the cost associated with transformations of quantum states, typically taken to be the number of copies of a given state, is instead identified with the size of the quantum memory needed to realize the protocol. Our approach allows for constructing transformation protocols that achieve strictly higher rates than those imposed by the relative entropy. Focusing on the task of resource distillation, we give broadly applicable strong converse bounds on the asymptotic rates of probabilistic distillation protocols, and show them to be tight in relevant settings such as entanglement distillation with nonentangling operations. This generalizes and extends previously known limitations that are only applicable to deterministic protocols. Our methods are based on recent results for probabilistic one-shot transformations as well as a new asymptotic equipartition property for the projective relative entropy.

关键词： Entanglement manipulation Quantum information processing Quantum information theory Resource theories

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Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives

Integrated Bi-Manual Motion Generation and Control shaped fo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonathan Vorndamme João Carvalho Riddhiman Laha Dorothea Koert Luis Figueredo Jan Peters Sami Haddadin Department of Computer Engineering Technische Universität München (TUM) TUM School of Computation Information and Technology (CIT) Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Intelligent Autonomous Systems group Technische Universitat Darmstadt (TUD) Germany Center for Cognitive Science TUD Germany

ISBN: (纸本)9798350309805

This work introduces a novel cooperative control framework that allows for real-time reactiveness and adaptation whilst satisfying implicit constraints stemming from proba-bilistic/stochastic trajectories. Stemming from task-oriented sampling and/or task-oriented demonstrations, e.g., learning based on motion primitives, such trajectories carry additional information often neglected during real-time control deployment. In particular, methods such as probabilistic movement primitives offer the advantage to capture the inherent stochasticity in human demonstrations - which in turn reflects human's understanding about task-variability and adaption possibilities. This information, however, is often poorly exploited and, mostly, used during offline trajectory planning stage. Our work instead introduces a novel real-time motion-generation strategy that explicitly exploits such information to improve trajectories according to changes in the environmental condition and robot task-space topology. The proposed solution is particularly well-suited for bi-manual and coordinated systems where the increased kinematic complexity, tightly-coupled constraints and reduced workspace have detrimental effects on the manipula-bility, joint-limits, and are even capable of causing unstable behavior and task-failure. Our methodology addresses these challenges, and improves performance and task-execution by taking the confidence range region explicitly into account whilst maneuvering towards better configurations. Furthermore, it can directly cope with different closed-chain kinematics and task-space topologies, resulting for instance from different grasps. Experimental evaluations on a bi-manual Franka panda robot show that the method can run in the inner control loop of the robot and enables successful execution of highly constrained tasks.

关键词： Trajectory planning Robot kinematics Humanoid robots Kinematics Probabilistic logic Real-time systems Trajectory

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Low-overhead pieceable fault-tolerant construction of logical controlled-phase circuit for degenerate quantum code*

Low-overhead pieceable fault-tolerant construction of logica...

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2020 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2020

作者： Lin, Chen Yang, Guowu Wu, Jinzhao Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning530006 China

ISBN: (纸本)9781728170046

We propose a non-transversal but pieceable fault-tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault-tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method. © 2020 IEEE.

关键词： Timing circuits

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Broadband switchable infrared absorbers using phase-change materials

arXiv

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arXiv 2023年

作者： Emrose, Md Tanvir Payne, Emily L. Chenglong, Y.O.U. Veronis, Georgios School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Physics Brigham Young University - Idaho RexburgID83460 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States

We introduce multilayer structures with the phase-change material Ge2Sb2Te5 (GST) for use as broadband switchable absorbers in the infrared wavelength range. We optimize both the material composition and the layer thicknesses of the multilayer structures, in order to maximize the difference between the absorption for GST in its crystalline phase and the absorption for GST in its amorphous phase in the wavelength range of interest. We show that in the optimized structures near perfect absorption can be switched to very low absorption in a broad wavelength range by switching GST from its crystalline to its amorphous phase. Our optimized lithography-free structures have better performance than harder-to-fabricate three-dimensional structures. Our results could pave the way to a new class of broadband switchable absorbers and thermal sources in the infrared wavelength range. © 2023, CC BY.

关键词： Phase change materials

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Removing EOG Artifacts from the Resting State EEG Signal of Methamphetamine Addicts by ICA Algorithms

Removing EOG Artifacts from the Resting State EEG Signal of ...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Zhejiang Lab Research Center for Intelligent Sensing Hangzhou China

EEG signal contains a wealth of information about brain activity, but the recording process is inevitably contaminated by EOG artifacts. An effective method to remove EOG artifacts can provide a guarantee for subsequent EEG analysis. In this paper, we compare the performance of four ICA algorithms in removing EOG artifacts from EEG signals of methamphetamine addicts. From the perspective of time domain and power spectral density, all the four algorithms can effectively remove the EOG artifacts without obvious difference. In terms of PSNR, MI and processing speed, FastICA algorithm can achieve higher processing speed and reconstruct signals better than the other three algorithms.

关键词： Electrooculography Brain Electroencephalography Brain-computer interfaces Recording Time-domain analysis

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Effects of Input Neuron Mapping Coordinates in Spiking neural Network on the Motor Imagery EEG Signals Classification

Effects of Input Neuron Mapping Coordinates in Spiking Neura...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Spiking neural network (SNN), the third generation of the artificial neural network, uses the same computational principles as Spatio-temporal brain data (STBD) generation, so it can be used as an effective tool to learn and understand STBD. When using SNN to process STBD, mapping of input neurons is a problem to be considered. This paper investigates the influence of changing the mapping coordinates of input neurons on motor imagery EEG signal classification. The results show that the coordinate change affects the interaction between the input neuron and its neighboring neurons, and causes the classification accuracy to change, but the impact mechanism is unknown.

关键词： Neurons Pattern classification Artificial neural networks Electroencephalography Brain-computer interfaces Biological neural networks

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

arXiv

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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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