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Improving unsupervised domain adaptation by reducing bi-level feature redundancy

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arXiv 2020年

作者： Wang, Mengzhu Zhang, Xiang Lan, Long Wang, Wei Tan, Huibin Luo, Zhigang Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China Institute for Quantum State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China DUT-RU International School of Information Science & Engineering Dalian University of Technology DalianLiaoning116000 China Department of Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignore this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific features with a transferable decorrelated normalization module, which preserves specific domain information whilst easing the side effect of feature redundancy on the sequel domain-invariance. In the second level, domain-invariant feature redundancy caused by domain-shared representation is further mitigated via an alternative brand orthogonality for better generalization. These two novel aspects can be easily plugged into any BN-based backbone neural networks. Specifically, simply applying them to ResNet50 has achieved competitive performance to the state-of-the-arts on five popular benchmarks. Our code will be available at https://***/dreamkily/gUDA. © 2020, CC BY.

关键词： Deep learning

Tripartite genuine non-gaussian entanglement in three-mode spontaneous parametric downconversion

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arXiv 2020年

作者： Agustí, A. Sandbo Chang, C.W. Quijandría, F. Johansson, G. Wilson, C.M. Sabín, C. Instituto de Física Fundamental CSIC Serrano 113-bis Madrid28006 Spain Institute for Quantum Computing and Electrical and Computer Engineering University of Waterloo Waterloo Canada Microtechnology and Nanoscience Chalmers University of Technology MC2 GöteborgSE-412 96 Sweden

We show that the states generated by a three-mode spontaneous parametric downconversion (SPDC) interaction Hamiltonian possess tripartite entanglement of a different nature to other paradigmatic three-mode entangled states generated by the combination of two-mode SPDCs interactions. While two-mode SPDC generates gaussian states whose entanglement can be characterized by standard criteria based on two-mode quantum correlations, these criteria fail to capture the entanglement generated by three-mode SPDC. We use criteria built from three-mode correlation functions to show that the class of states recently generated in a superconductingcircuit implementation of three-mode SPDC ideally have tripartite entanglement, contrary to recent claims in the literature. These criteria are suitable for triple SPDC but we show that they fail to detect tripartite entanglement in other states which are known to possess it, which illustrates the existence of two fundamentally different notions of tripartite entanglement in three-mode continuous variable systems. Copyright © 2020, The Authors. All rights reserved.

关键词： quantum entanglement

Hybrid quantum-classical approach to enhanced quantum metrology

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arXiv 2020年

作者： Yang, Xiaodong Chen, Xi Li, Jun Peng, Xinhua Laflamme, Raymond Hefei National Laboratory for Physical Sciences at the Microscale Department of Modern Physics University of Science and Technology of China Hefei230026 China Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China CAS Key Laboratory of Microscale Magnetic Resonance University of Science and Technology of China Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada

quantum metrology plays a fundamental role in many scientific areas, and it concerns how to manipulate the given resources to achieve the best precision of the to-be-estimated parameter. Usually, the highest precision can be reached by performing optimal probe states and the corresponding optimal measurements under prefixed dynamics. However, the complexity of engineering entangled probes and the external noise raise technological barriers for its realization. Here, we address this problem by introducing adjustable controls into the encoding process and then utilizing a hybrid quantum-classical approach to automatically optimize the controls online. Our scheme does not require any complex or intractable off-line design, and it can inherently correct certain errors during the learning procedure. We also report the first experimental demonstration of this promising scheme for the task of frequency estimation on a nuclear magnetic resonance (NMR) processor. The proposed scheme paves the way to experimentally auto-search optimal protocol for improving the metrology precision. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency estimation

Possible ‘symmetry-imposed’ near-nodal two-dimensional p-wave pairing in Sr2RuO4

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arXiv 2019年

作者： Li, Yu Huang, Wen Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518005 China

One key feature of the multi-orbital superconducting Sr2RuO4 is the presence of nodal or near-nodal quasiparticle excitations revealed in a wide variety of experiments. Typically, a nodal gap structure in a two-dimensional model would be inconsistent with the chiral or helical p-wave interpretations. However, we demonstrate that true gap nodes may emerge along the x- and y-directions on the quasi-one-dimensional Fermi surfaces, if the multi-orbital chiral or helical p-wave pairings acquire peculiar forms wherein the dxz and dyz orbitals develop ky- and kx-like pairings, respectively. Spin-orbit coupling η induces a near-nodal gap of order η2/W2∆0, where ∆0 is the gap amplitude and W roughly the bandwidth. Provided the aforementioned pairing is predominant, the near-nodal gap structure is robust upon the inclusion of other multi-orbital pairings that share the same symmetries. In light of the recent experimental progresses, our proposal suggests that two-dimensional p-wave pairings may still be viable candidate ground states for Sr2RuO4. A near-nodal helical p-wave order, for example, may also be in line with the substantial drop in the NMR Knight shift under an in-plane magnetic field. Copyright © 2019, The Authors. All rights reserved.

关键词： Strontium compounds

Ultra-bright multiplexed energy-time entangled photon generation from lithium niobate on insulator chip

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arXiv 2020年

作者： Xue, Guang-Tai Niu, Yun-Fei Liu, Xiaoyue Duan, Jia-Chen Chen, Wenjun Pan, Ying Jia, Kunpeng Wang, Xiaohan Liu, Hua-Ying Zhang, Yong Xu, Ping Zhao, Gang Cai, Xinlun Gong, Yan-Xiao Hu, Xiaopeng Xie, Zhenda Zhu, Shining National Laboratory of Solid State Microstructures School of Physics School of Electronic Science and Engineering College of Engineering and Applied Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China State Key Laboratory of Optoelectronic Materials and Technologies School of Physics and Engineering Sun Yat-sen University Guangzhou510275 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha410073 China

High-flux entangled photon source is the key resource for quantum optical study and application. Here it is realized in a lithium niobate on isolator (LNOI) chip, with 2.79×1011 Hz/mW photon pair rate and 1.53×109 Hz/nm/mW spectral brightness. These data are boosted by over two orders of magnitude compared to existing technologies. A 130-nm broad bandwidth is engineered for 8-channel multiplexed energy-time entanglement. Harnessed by high-extinction frequency correlation and Franson interferences up to 99.17% visibility, such energy-time entanglement multiplexing further enhances high-flux data rate, and warrants broad applications in quantum information processing on a chip. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

Investigations of magneto-elastic coupling in a multiferroic ferrite by in-situ precession diffraction

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Microscopy and Microanalysis 2021年第S1期27卷 2166-2168页

作者： Shiqing Deng Shengdong Sun Ping Miao Jun Li Chengchao Xu Wenbin Wang Yimei Zhu Jun Chen Jing Zhu School of Mathematics and Physics University of Science and Technology Beijing Beijing China Department of Condensed Matter Physics and Materials Science Brookhaven National Laboratory Upton NY USA School of Materials Science and Engineering Tsinghua University Beijing China Beijing United States Beijing Advanced Innovation Center for Materials Genome Engineering and Department of Physical Chemistry University of Science and Technology Beijing Beijing China United States Institute of High Energy Physics Chinese Academy of Sciences Beijing China Spallation Neutron Source Science Center Dongguan China United States Institute of Physics Chinese Academy of Sciences Beijing China United States Institute of Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China United States Department of Condensed Matter Physics and Materials Science Brookhaven National Laboratory Upton NY USA United States School of Materials Science and Engineering Tsinghua University Beijing China United States

Towards a variational Jordan-Lee-Preskill quantum algorithm

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arXiv 2021年

作者： Liu, Junyu Li, Zimu Zheng, Han Yuan, Xiao Sun, Jinzhao Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA91125 United States Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Chicago Quantum Exchange ChicagoIL60637 United States Kadanoff Center for Theoretical Physics The University of Chicago ChicagoIL60637 United States DAMTP Center for Mathematical Sciences University of Cambridge CambridgeCB30WA United Kingdom Department of Statistics The University of Chicago ChicagoIL60637 United States Center on Frontiers of Computing Studies Peking University Beijing100871 China Stanford Institute for Theoretical Physics Stanford University StanfordCA94306 United States Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom

Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 dimensional λφ4 quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational quantum circuit (quantum neural network) analogs of the Jordan-Lee-Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the LSZ reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the "spectral crowding" problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational efficiency

DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

Wasserstein complexity of quantum circuits

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Journal of Physics A: Mathematical and Theoretical 2025年第26期58卷 265302-265302页

作者： Lu Li Kaifeng Bu Dax Enshan Koh Arthur Jaffe Seth Lloyd Department of Mathematics The Ohio State University Columbus OH 43210 United States of America Department of Physics Harvard University Cambridge MA 02138 United States of America Quantum Innovation Centre (Q.InC) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore Department of Mathematics Harvard University Cambridge MA 02138 United States of America Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 United States of America

Given a unitary transformation, what is the size of the smallest quantum circuit that implements it? This quantity, known as the quantum circuit complexity, is a fundamental property of quantum evolutions that has widespread applications in many fields, including quantum computation, quantum field theory, and black hole physics. In this paper, we obtain a new lower bound for the quantum circuit complexity in terms of a novel complexity measure that we propose for quantum circuits, which we call the quantum Wasserstein complexity. Our proposed measure is based on the quantum Wasserstein distance of order one (also called the quantum earth mover's distance), a metric on the space of quantum states. We also prove several fundamental and important properties of our new complexity measure, which stand to be of independent interest. Finally, we show that our new measure also provides a lower bound for the experimental cost of implementing quantum circuits, which implies a quantum limit on converting quantum resources to computational resources. Our results provide novel applications of the quantum Wasserstein distance and pave the way for a deeper understanding of the resources needed to implement a quantum computation.

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