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quantum learning advantage on a scalable photonic platform

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arXiv 2025年

作者： Liu, Zheng-Hao Brunel, Romain Østergaard, Emil E.B. Cordero, Oscar Chen, Senrui Wong, Yat Nielsen, Jens A.H. Bregnsbo, Axel B. Zhou, Sisi Huang, Hsin-Yuan Oh, Changhun Jiang, Liang Preskill, John Neergaard-Nielsen, Jonas S. Andersen, Ulrik L. Department of Physics Technical University of Denmark Fysikvej Kongens Lyngby 2800 Denmark Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy Institute for Quantum Computing University of Waterloo ONN2L 2Y5 Canada Google Quantum AI VeniceCA United States Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Recent advancements in quantum technologies have opened new horizons for exploring the physical world in ways once deemed impossible. Central to these breakthroughs is the concept of quantum advantage, where quantum systems outperform their classical counterparts in solving specific tasks. While much attention has been devoted to computational speedups, quantum advantage in learning physical systems remains a largely untapped frontier. Here, we present a photonic implementation of a quantum-enhanced protocol for learning the probability distribution of a multimode bosonic displacement process. By harnessing the unique properties of continuous-variable quantum entanglement, we obtain a massive advantage in sample complexity with respect to conventional methods without entangled resources. With approximately 5 dB of two-mode squeezing—corresponding to imperfect Einstein–Podolsky–Rosen (EPR) entanglement—we learn a 100-mode bosonic displacement process using 11.8 orders of magnitude fewer samples than a conventional scheme. Our results demonstrate that even with non-ideal, noisy entanglement, a significant quantum advantage can be realized in continuous-variable quantum systems. This marks an important step towards practical quantum-enhanced learning protocols with implications for quantum metrology, certification, and machine learning. Copyright © 2025, The Authors. All rights reserved.

关键词： quantum entanglement

quantum conditional mutual information of W state in non-inertial frames

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arXiv 2023年

作者： Saveetha, H. Rohde, Peter P. Chandrashekar, R. Centre for Quantum Science and Technology Chennai Institute of Technology Chennai600069 India University of Technology Sydney UltimoNSW2007 Australia Hearne Institute for Theoretical Physics Department of Physics & Astronomy Louisiana State University Baton RougeLA United States Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Pudong Shanghai200124 China Centre for Quantum Information Communication and Computing Indian Institute of Technology Madras Chennai600036 India

quantum conditional mutual information (QCMI) is a versatile information theoretic measure. It is used to find the amount of correlations between two qubits from the perspective of a third qubit. In this work we characterise the QCMI of tripartite W-states when some of the qubits are under accelerated motion. Here for our investigations we consider a massless fermionic field in the single mode approximation. We consider all possible situations with respect to acceleration of the qubits. From our results we observe that QCMI can either increase or decrease depending on the role of the qubit being accelerated. Finally we discuss the connection between QCMI and correlations by studying the biseparable and separable states. © 2023, CC BY.

关键词： Information theory

Generation of Schrödinger Cat States with Wigner Negativity Using a Low-Loss Continuous-Wave Optical Parametric Amplifier

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Generation of Schrödinger Cat States with Wigner Negativity...

CLEO: QELS_Fundamental Science, QELS 2022

作者： Kawasaki, Akito Takase, Kan Jeong, Byung Kyu Kashiwazaki, Takahiro Kazama, Takushi Enbutsu, Koji Watanabe, Kei Umeki, Takeshi Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Asavanant, Warit Endo, Mamoru Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

We observed Wigner negativity of Schrödinger cat states generated with a low-loss continuous-wave optical parametric amplifier, paving the way toward fault-tolerant and universal quantum computation with terahert... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical parametric amplifiers

Spintronic devices for neuromorphic computing

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Science China(Physics,Mechanics & Astronomy) 2020年第7期63卷 124-126页

作者： YaJun Zhang Qi Zheng XiaoRui Zhu Zhe Yuan Ke Xia Center for Advanced Quantum Studies and Department of Physics Beijing Normal UniversityBejing 100875China Peng Cheng Laboratory Center for Quantum ComputingShenzhen 518005China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China

In the past decades,significant progress has been achieved in artificial intelligence,which is now widely applied in image recognition,big data analysis,unmanned vehicle control and other cognitive tasks[1].These applications nevertheless are highly energy consuming partly because of the mismatch between the neural-network-computing-based software im-plementation and the von Neumann architecture of present *** promising solution is developing neuro-morphic chips without the so-called von Neumann bottle-neck,which are suitable for performing the desired computation based on artificial neural networks(ANNs).

关键词： consuming desired computing

DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

Randomness-enhanced expressivity of quantum neural networks

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arXiv 2023年

作者： Wu, Yadong Yao, Juan Zhang, Pengfei Li, Xiaopeng Department of Physics Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200438 China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai200232 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China International Quantum Academy Guangdong Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shanghai Research Center for Quantum Sciences Shanghai201315 China

As a hybrid of artificial intelligence and quantum computing, quantum neural networks (QNNs) have gained significant attention as a promising application on near-term, noisy intermediate-scale quantum (NISQ) devices. Conventional QNNs are described by parametrized quantum circuits, which perform unitary operations and measurements on quantum states. In this work, we propose a novel approach to enhance the expressivity of QNNs by incorporating randomness into quantum circuits. Specifically, we introduce a random layer, which contains single-qubit gates sampled from an trainable ensemble pooling. The prediction of QNN is then represented by an ensemble average over a classical function of measurement outcomes. We prove that our approach can accurately approximate arbitrary target operators using Uhlmann's theorem for majorization, which enables observable learning. Our proposal is demonstrated with extensive numerical experiments, including observable learning, Rényi entropy measurement, and image recognition. We find the expressivity of QNNs is enhanced by introducing randomness for multiple learning tasks, which could have broad application in quantum machine learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Image recognition

Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

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SSRN

SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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

Nematic Ising superconductivity with hidden magnetism in few-layer 6R-TaS2

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arXiv 2024年

作者： Liu, Shao-Bo Tian, Congkuan Fang, Yuqiang Rong, Hongtao Cao, Lu Wei, Xinjian Cui, Hang Chen, Mantang Chen, Di Song, Yuanjun Cui, Jian Li, Jiankun Guan, Shuyue Jia, Shuang Chen, Chaoyu He, Wenyu Huang, Fuqiang Jiang, Yuhang Mao, Jinhai Xie, X.C. Law, K.T. Chen, Jian-Hao International Center for Quantum Materials School of Physics Peking University Beijing China Beijing Academy of Quantum Information Sciences Beijing China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen China School of Physical Science and Technology ShanghaiTech University Shanghai China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China College of Materials Science and Optoelectronic Technology Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing China Key Laboratory for the Physics and Chemistry of Nanodevices Peking University Beijing China Hefei National Laboratory Hefei China Department of Physics Hong Kong University of Science and Technology Hong Kong

In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T*) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Superconducting materials