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Hybrid quantum-classical convolutional neural networks

Science China(physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

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Photonic quantum Receiver Attaining the Helstrom Bound

arXiv

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arXiv 2024年

作者： Warke, Aakash Nötzel, Janis Takase, Kan Asavanant, Warit Nagayoshi, Hironari Fukui, Kosuke Takeda, Shuntaro Furusawa, Akira van Loock, Peter Department of Physics Johannes Gutenberg Universität Mainz Institute of Physics Staudingerweg 7 Mainz55128 Germany Emmy Noether Group for Theoretical Quantum Systems Design Technical University of Munich MunichD-80333 Germany 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

We propose an efficient decomposition scheme for a quantum receiver that attains the Helstrom bound in the low-photon regime for discriminating binary coherent states. Our method, which avoids feedback as used in Dolinar’s case, breaks down nonlinear operations into basic gates used in continuous-variable quantum computation. We account for realistic conditions by examining the impact of photon loss and imperfect photon detection, including the presence of dark counts, while presenting squeezing as a technique to mitigate these noise sources and maintain the advantage over SQL. Our scheme motivates testing quantum advantages with cubic-phase gates and designing photonic quantum computers to optimize symbol-by-symbol measurements in optical communication. © 2024, CC BY.

关键词： Photons

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拓扑异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)高压诱导超导电性研究

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Science China Materials 2023年第7期66卷 2822-2828页

作者：裴翠颖朱鹏李炳谈赵毅高玲玲李昌华朱世豪张庆华应天平谷林高波缑慧阳姚延荪孙建刘寒雨陈宇林王秩伟姚裕贵齐彦鹏 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of Technology Beijing 100081China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of TechnologyBeijing 100081China Material Science Center Yangtze Delta Region Academy of Beijing Institute of TechnologyJiaxing 314011China State Key Laboratory of Superhard Materials and International Center for Computational Method and Software College of PhysicsJilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China 7 Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing 100190China Center for High Pressure Science and Technology Advanced Research Beijing 100094China Department of Physics and Engineering Physics University of SaskatchewanSaskatoonSaskatchewan S7N 5E2Canada National Laboratory of Solid State Microstructures School of Physics and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China ShanghaiTech Laboratory for Topological Physics ShanghaiTech UniversityShanghai 201210China Department of Physics Clarendon LaboratoryUniversity of OxfordParks RoadOxford OX13PUUK Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech UniversityShanghai 201210China

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急... 详细信息

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急剧下降.值得注意的是,当进一步施加压力到30 GPa以上时,出现了一种新的超导态,且T_(c)直到本实验最高压力仍未饱和.结合XRD和拉曼光谱,我们认为(PbSe)_(5)(Bi_(2)Se_(3))_(6)中两个超导态的出现与压力诱导的结构相变有关.拓扑异质结压力下出现新的量子态,为进一步研究拓扑和超导的关系提供了一个良好的平台.

关键词： high pressure superconductivity heterostructure topological material

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Recent Developments on MECSELs: Multi-type quantum Well Gain Structures for Widely Tunable Continuous Wave Operation and a Non-Resonant Sub-Cavity Design

Recent Developments on MECSELs: Multi-type Quantum Well Gain...

引用

Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Hermann Kahle Patrik Rajala Philipp Tatar-Mathes Mircea Guina Department of Physics Center for Optoelectronics and Photonics Paderborn Institute for Photonic Quantum Systems (PhoQS) Paderborn University Paderborn Germany Optoelectronics Research Centre (ORC) Physics Unit / Photonics Faculty of Engineering and Natural Science Tampere University Tampere Finland

ISBN: (纸本)9798350345995

Membrane external-cavity surface-emitting lasers (MECSELs [1], [2]) are pushing the performance limits of vertically emitting semiconductor lasers. Their simple idea of using just a very thin (hundreds of nanometers to few microns) gain membrane and no monolithically integrated distributed Bragg reflector (DBR) opens up new possibilities through efficient double-side optical pumping and superior heat extraction [3] from the active area. One such possibility is the introduction of multiple types of quantum wells (QWs) to a single laser gain structure. We present this new design strategy and demonstrate how it can be used to create broadband, high power membrane external-cavity surface-emitting laser gain structures that can potentially generate a flat emission power spectrum. The emphasis in this strategy is on ensuring sufficient gain over a wide wavelength range, having efficient pump absorption, and restricted carrier mobility between the different QWs during laser operation. Latest results show > 86 nm (> 26 THz) tuning range (see Fig. 1a), but still 70 nm (> 21 THz) FWHM tuning range (see Fig. 1b) with more than 125 mW of power through the whole tuning range at room temperature operation.

关键词： Bragg reflectors Laser operation Multiple quantum wells Optical pumping quantum wells Semiconductor lasers

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Nano device fabrication for in-memory and in-sensor reservoir computing

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International Journal of Extreme Manufacturing 2025年第1期7卷 46-71页

作者： Yinan Lin Xi Chen Qianyu Zhang Junqi You Renjing Xu Zhongrui Wang Linfeng Sun Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of TechnologyBeijing 100081People’s Republic of China Beijing Key Laboratory of Nanophotonics&Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of TechnologyBeijing 100081People’s Republic of China Department of Electrical and Electronic Engineering The University of Hong KongPokfulam RoadHong Kong Special Administrative Region of ChinaPeople’s Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kong Special Administrative Region of ChinaPeople’s Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055People’s Republic of China Thrust of Microelectronics of Function Hub The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400People’s Republic of China

Recurrent neural networks(RNNs)have proven to be indispensable for processing sequential and temporal data,with extensive applications in language modeling,text generation,machine translation,and time-series *** their versatility,RNNs are frequently beset by significant training expenses and slow convergence times,which impinge upon their deployment in edge AI *** computing(RC),a specialized RNN variant,is attracting increased attention as a cost-effective alternative for processing temporal and sequential data at the ***’s distinctive advantage stems from its compatibility with emerging memristive hardware,which leverages the energy efficiency and reduced footprint of analog in-memory and in-sensor computing,offering a streamlined and energy-efficient *** review offers a comprehensive explanation of RC’s underlying principles,fabrication processes,and surveys recent progress in nano-memristive device based RC systems from the viewpoints of in-memory and in-sensor RC *** covers a spectrum of memristive device,from established oxide-based memristive device to cutting-edge material science developments,providing readers with a lucid understanding of RC’s hardware implementation and fostering innovative designs for in-sensor RC ***,we identify prevailing challenges and suggest viable solutions,paving the way for future advancements in in-sensor RC technology.

关键词： reservoir computing memristive device fabrication compute-in-memory in-sensor computing

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Single-boson exchange formulation of the Schwinger–Dyson equation and its application to the functional renormalization group

arXiv

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arXiv 2024年

作者： Patricolo, Miriam Gievers, Marcel Fraboulet, Kilian Al-Eryani, Aiman Heinzelmann, Sarah Bonetti, Pietro M. Toschi, Alessandro Vilardi, Demetrio Andergassen, Sabine Institute of Information Systems Engineering Vienna University of Technology Vienna Austria Institute for Solid State Physics Vienna University of Technology Vienna Austria Max Planck Institute for Solid State Research Heisenbergstrasse 1 Stuttgart Germany Arnold Sommerfeld Center for Theoretical Physics Center for NanoScience Munich Center for Quantum Science and Technology Ludwig-Maximilians-Universität München München Germany Max Planck Institute of Quantum Optics Garching Germany Institute for Theoretical Physics Center for Quantum Science Universität Tübingen Tübingen Germany Theoretical Physics III Ruhr-University Bochum Bochum44801 Germany Department of Physics Harvard University CambridgeMA02138 United States

We extend the recently introduced single-boson exchange formulation to the computation of the self-energy from the Schwinger–Dyson equation (SDE). In particular, we derive its expression both in diagrammatic and in physical channels. The simple form of the single-boson exchange SDE, involving only the bosonic propagator and the fermion-boson vertex, but not the rest function, allows for an efficient numerical implementation. We furthermore discuss its implications in a truncated unity solver, where a restricted number of form factors introduces an information loss in the projection of the momentum dependence that in general affects the equivalence between the different channel representations. In the application to the functional renormalization group, we find that the convergence in the number of form factors depends on the channel representation of the SDE. For the two-dimensional Hubbard model at weak coupling, the pseudogap opening driven by antiferromagnetic fluctuations is captured already by a single (s-wave) form factor in the magnetic channel representation, differently to the density and superconducting channels. © 2024, CC BY.

关键词： Bosons

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Fisher information rates in sequentially measured quantum systems

arXiv

引用

arXiv 2024年

作者： O’Connor, Eoin Campbell, Steve Landi, Gabriel T. School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Belfield Dublin 4 Ireland Dahlem Center for Complex Quantum Systems Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Department of Physics and Astronomy University of Rochester RochesterNY14627 United States

We consider the impact that temporal correlations in the measurement statistics can have on the achievable precision in a sequential metrological protocol. In this setting, and for a single quantum probe, we establish that it is the transitions between the measurement basis states that plays the most significant role in determining the precision, with the resulting conditional Fisher information being interpretable as a rate of information acquisition. Projective measurements are shown to elegantly demonstrate this in two disparate estimation settings. Firstly, in determining the temperature of an environment and, secondly, to ascertain a parameter of the system Hamiltonian. In both settings we show that the sequential estimation approach can provide a useful method to enhance the achievable precision. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Indirect influence in social networks as an induced percolation phenomenon (vol 119, e2100151119, 2022)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第24期119卷 e2100151119-e2100151119页

作者： Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing School of Computer Science and Engineering Sun Yat-sen University 510006 Guangzhou China Institute of Future Networks Southern University of Science and Technology 518055 Shenzhen China Peng Cheng Laboratory 518066 Shenzhen China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University 510006 Guangzhou China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University 510006 Guangzhou China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Institute for Biocomputation and Physics of Complex Systems University of Zaragoza 50018 Zaragoza Spain Department of Theoretical Physics University of Zaragoza 50018 Zaragoza Spain ISI Foundation 10126 Torino Italy Department of Statistics and Data Science College of Science Southern University of Science and Technology 518055 Shenzhen China

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

关键词： percolation indirect interactions social network phase transition behavioral contagion

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Two-dimensional materials for future information technology: status and prospects

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Science China(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng XU Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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Robust Weak Topological Insulator in the Bismuth Halide Bi4Br2I2

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Physical Review Letters 2024年第8期133卷 086602页

作者： Ryo Noguchi Masaru Kobayashi Kaishu Kawaguchi Wataru Yamamori Kohei Aido Chun Lin Hiroaki Tanaka Kenta Kuroda Ayumi Harasawa Viktor Kandyba Mattia Cattelan Alexei Barinov Makoto Hashimoto Donghui Lu Masayuki Ochi Takao Sasagawa Takeshi Kondo Institute for Solid State Physics (ISSP) Laboratory for Materials and Structures International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM Graduate School of Advanced Science and Engineering S.S.14 163.5 km Basovizza Trieste Italy SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics Forefront Research Center Research Center for Autonomous Systems Materialogy Trans-scale Quantum Science Institute

We apply a topological material design concept for selecting a bulk topology of 3D crystals by different van der Waals stackings of 2D topological insulator layers, and find a bismuth halide Bi4Br2I2 to be an ideal weak topological insulator (WTI) with the largest band gap (∼300 meV) among all the WTI candidates, by means of angle-resolved photoemission spectroscopy (ARPES), density functional theory (DFT) calculations, and resistivity measurements. Furthermore, we reveal that the topological surface state of a WTI is not “weak” but rather robust against external perturbations against the initial theoretical prediction by performing potassium deposition experiments. Our results vastly expand future opportunities for fundamental research and device applications with a robust WTI.

关键词： Electronic structure Topological insulators Angle-resolved photoemission spectroscopy

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