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arXiv 2025年

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Takasu, Sachiko Fukuda, Daiji Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan National Institute of Advanced Industrial Science and Technology 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory 1-1-1 Umezono Ibaraki Tsukuba305-8563 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Physics University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst 100 Natural Resources Rd AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

Generating logical qubits, essential for error detection and correction in quantum computation, remains a critical challenge in continuous-variable (CV) optical quantum information processing. The Gottesman-Kitaev-Preskill (GKP) code is a leading candidate for logical qubits, and its generation requires large-amplitude coherent state superpositions—Schrödinger cat states. However, experimentally producing these resource states has been hindered in the optical domain by technical challenges. The photon subtraction method, a standard approach for generating cat states using a squeezed vacuum and a photon number-resolving detector, has proven difficult to scale to multi-photon operations. While the amplitude of the generated cat states increases with the number of subtracted photons, limitations in the generation rate have restricted the maximum photon subtraction to n = 3 for over a decade. In this work, we demonstrate high-rate photon subtraction of up to four photons from a squeezed vacuum with picosecond wavepackets generated by a broadband optical parametric amplifier. Using a Ti-Au superconducting-transition-edge sensor, we achieve high-speed, high-resolution photon number discrimination. The resulting states exhibit Wigner function negativity without loss correction, and their quantum coherence is verified through off-diagonal density matrix elements in CV representation. These results overcome long-standing limitations in multi-photon operations, providing a critical foundation for generating quantum resources essential for fault-tolerant quantum computing and advancing ultrafast optical quantum processors. Copyright © 2025, The Authors. All rights reserved.

关键词： Parametric amplifiers

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Acceleration-Induced Effects in Stimulated Light-Matter Interactions

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Physical Review Letters 2022年第16期128卷 163603-163603页

作者： Barbara Šoda Vivishek Sudhir Achim Kempf Department of Physics University of Waterloo Waterloo Ontario N2L 3G1 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA LIGO Laboratory Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Applied Mathematics University of Waterloo Waterloo Ontario N2L 3G1 Canada Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada

We show that, in addition to the Unruh effect, there exist two new phenomena that are due to acceleration in the quantum theory of the light-matter interaction. The first is the phenomenon of acceleration-induced transparency which arises since acceleration impacts not only the counter-rotating terms in the light-matter interaction (the cause of the conventional Unruh effect) but also the rotating wave terms. The second new phenomenon is that the Unruh effect can be stimulated, a phenomenon that arises since not only rotating-wave terms can be stimulated (as in conventional stimulated emission) but also counter-rotating terms. The new phenomena are potentially strong enough to be experimentally observable.

关键词： Quantum description of light-matter interaction Relativistic & quantum electrodynamic effects in atoms, molecules,& ions Spontaneous emission Unruh effect

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Linear combination of Hamiltonian simulation for nonunitary dynamics with optimal state preparation cost

arXiv

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arXiv 2023年

作者： An, Dong Liu, Jin-Peng Lin, Lin Joint Center for Quantum Information and Computer Science University of Maryland MD20742 United States Department of Mathematics University of California BerkeleyCA94720 United States Simons Institute for the Theory of Computing University of California BerkeleyCA94720 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Challenge Institute for Quantum Computation University of California BerkeleyCA94720 United States

We propose a simple method for simulating a general class of non-unitary dynamics as a linear combination of Hamiltonian simulation (LCHS) problems. LCHS does not rely on converting the problem into a dilated linear system problem, or on the spectral mapping theorem. The latter is the mathematical foundation of many quantum algorithms for solving a wide variety of tasks involving non-unitary processes, such as the quantum singular value transformation. The LCHS method can achieve optimal cost in terms of state preparation. We also demonstrate an application for open quantum dynamics simulation using the complex absorbing potential method with near-optimal dependence on all parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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Hybrid Spin and Anomalous Spin-Momentum Locking in Surface Elastic Waves

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Physical Review Letters 2023年第13期131卷 136102-136102页

作者： Chenwen Yang Danmei Zhang Jinfeng Zhao Wenting Gao Weitao Yuan Yang Long Yongdong Pan Hong Chen Franco Nori Konstantin Y. Bliokh Zheng Zhong Jie Ren Center for Phononics and Thermal Energy Science China-EU Joint Lab on Nanophononics Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai 200092 China School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai 200092 China School of Mechanics and Aerospace Engineering Southwest Jiaotong University Chengdu Sichuan 610031 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wako-shi Saitama 351-0198 Japan Center for Quantum Computing RIKEN Wako-shi Saitama 351-0198 Japan Physics Department University of Michigan Ann Arbor Michigan 48109-1040 USA Centre of Excellence ENSEMBLE3 Sp. z o.o. 01-919 Warsaw Poland Donostia International Physics Center (DIPC) Donostia-San Sebastián 20018 Spain

Transverse spin of surface waves is a universal phenomenon which has recently attracted significant attention in optics and acoustics. It appears in gravity water waves, surface plasmon polaritons, surface acoustic waves, and exhibits remarkable intrinsic spin-momentum locking, which has found useful applications for efficient spin-direction couplers. Here we demonstrate, both theoretically and experimentally, that the transverse spin of surface elastic (Rayleigh) waves has an anomalous sign near the surface, opposite to that in the case of electromagnetic, sound, or water surface waves. This anomalous sign appears due to the hybrid (neither transverse nor longitudinal) nature of elastic surface waves. Furthermore, we show that this sign anomaly can be employed for the selective spin-controlled excitation of symmetric and antisymmetric Lamb modes propagating in opposite directions in an elastic plate. Our results pave the way for spin-controlled manipulation of elastic waves and can be important for a variety of areas, from phononic spin-based devices to seismic waves.

关键词： Acoustic wave phenomena Angular momentum of light Elastic waves Elasticity Surface states Chiral symmetry Spin Elastic wave theory Surface acoustic wave

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A Triply-Periodic-Minimal-Surface Structured Interphase based on Fluorinated Polymers Strengthening High-energy Lithium Metal Batteries

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Angewandte Chemie 2024年第20期136卷

作者： Cong Ma Prof. Shihui Zou Yuxuan Wu Ke Yue Xiaohan Cai Prof. Yao Wang Prof. Jianwei Nai Prof. Tianqi Guo Prof. Xinyong Tao Prof. Yujing Liu College of Materials Science and Engineering Zhejiang University of Technology Hangzhou 310014 China Key Lab of Applied Chemistry of Zhejiang Province Department of Chemistry Zhejiang University Hangzhou 310027 China Department of Advanced Materials and Computing International Iberian Nanotechnology Laboratory (INL) 4715-330 Braga Portugal

The challenge of constructing a mechanically robust yet lightweight artificial solid-electrolyte interphase layer on lithium (Li) anodes highlights a trade-off between high battery safety and high energy density. Inspired by the intricate microstructure of the white sea urchin, we first develop a polyvinyl fluoride-hexafluoropropylene (PVDF-HFP) interfacial layer with a triple periodic minimal surface structure (TPMS) that could offer maximal modulus with minimal weight. This design endows high mechanical strength to an ordered porous structure, effectively reduces local current density, polarization, and internal resistance, and stabilizes the anode interface. At a low N/P ratio of ~3, using LiFePO 4 as the cathode, Li anodes protected by TPMS-structured PVDF-HFP achieve an extremely low capacity-fading-rate of approximately 0.002 % per cycle over 200 cycles at 1 C, with an average discharge capacity of 142 mAh g −1 . Meanwhile, the TPMS porous structure saves 50 wt % of the interfacial layer mass, thereby enhancing the energy density of the battery. The TPMS structure is conducive to large-scale additive manufacturing, which will provide a reference for the future development of lightweight, high-energy-density secondary batteries.

关键词： lithium metal batteries TPMS interphase design high energy density

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Quantum Electrodynamics in a Topological Waveguide

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Physical Review X 2021年第1期11卷 011015-011015页

作者： Eunjong Kim Xueyue Zhang Vinicius S. Ferreira Jash Banker Joseph K. Iverson Alp Sipahigil Miguel Bello Alejandro González-Tudela Mohammad Mirhosseini Oskar Painter Thomas J. Watson Sr. Laboratory of Applied Physics and Kavli Nanoscience Institute California Institute of Technology Pasadena California 91125 USA Institute for Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA AWS Center for Quantum Computing Pasadena California 91125 USA Instituto de Ciencia de Materiales de Madrid CSIC Madrid 28049 Spain Instituto de Física Fundamental IFF-CSIC Calle Serrano 113b Madrid 28006 Spain Gordon and Betty Moore Laboratory of Engineering California Institute of Technology Pasadena California 91125 USA

While designing the energy-momentum relation of photons is key to many linear, nonlinear, and quantum optical phenomena, a new set of light-matter properties may be realized by employing the topology of the photonic bath itself. In this work we experimentally investigate the properties of superconducting qubits coupled to a metamaterial waveguide based on a photonic analog of the Su-Schrieffer-Heeger model. We explore topologically induced properties of qubits coupled to such a waveguide, ranging from the formation of directional qubit-photon bound states to topology-dependent cooperative radiation effects. Addition of qubits to this waveguide system also enables direct quantum control over topological edge states that form in finite waveguide systems, useful for instance in constructing a topologically protected quantum communication channel. More broadly, our work demonstrates the opportunity that topological waveguide-QED systems offer in the synthesis and study of many-body states with exotic long-range quantum correlations.

关键词： Cavity quantum electrodynamics Metamaterials Quantum description of light-matter interaction Quantum optics with artificial atoms Quantum simulation Superconducting quantum optics Symmetry protected topological states Topological effects in photonic systems

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AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit’s Activation via Adjoint Operators

arXiv

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arXiv 2021年

作者： Wan, Qing Cheung, Siu Wun Choe, Yoonsuck School of Computer Science and Technology Zhejiang Gongshang University Zhejiang Province 310018 China Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States

Adjoint operators have been found to be effective in the exploration of CNN’s inner workings [1]. However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface. Such hypersurface can be computed for an arbitrary unit in the CNN, and we prove that this reconstructed hypersurface, when multiplied by the original input (through an inner product), will precisely replicate the output value of each unit. We show experimental results based on the CIFAR-10 and CIFAR-100 data sets where the proposed approach achieves near 0 activation value reconstruction error. Copyright © 2021, The Authors. All rights reserved.

关键词： Chemical activation

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Quantum computing hardware for HEP algorithms and sensing

arXiv

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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

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Effect of endogenous and anthropogenic factors on the alkalinisation and salinisation of freshwater in United States by using explainable machine learning

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Wimalagunarathna, N.D. Dharmarathne, Gangani Ekanayake, I.U. Rathanayake, Upaka Alwatugoda, Janaka Meddage, D.P.P. Department of Zoology Faculty of Applied Sciences University of Sri Jayewardenepura Gangodawila Nugegoda Sri Lanka Australian Laboratory Services Global Water and Hydrographic Hume 2620 ACT Australia School of Computing Technologies Royal Melbourne Institute of Technology Melbourne VIC Australia Ceylon Institute for Artificial Intelligence Research (CIAIR) Colombo Sri Lanka Department of Civil Engineering and Construction Faculty of Engineering and Design Atlantic Technological University Sligo Ireland Research and Innovations Centers Division Rabdan Academy Abu Dhabi United Arab Emirates Institute for Integrated and Intelligent Systems Griffith University Nathan QLD Australia School of Engineering and Technology The University of New South Wales Canberra 2600 ACT Australia

Freshwater salinisation and alkalinisation strongly depend on human and natural factors. We used an explainable machine learning approach to investigate the impact of natural and human factors on the salinity and alkalinity in rivers in the United States. The model achieved coefficient of determination of 0.89 for salinity predictions and 0.92 for alkalinity predictions. Shapley additive explanations revealed salinisation is driven mainly by human factors like population density (18 %) and impervious surface percentage (13 %) and natural factors like run-off (14 %). The alkalinisation is mainly influenced by natural processes such as runoff (36 %) and soil-pH (20 %). © 2024 The Authors

关键词： Alkalinity Ecosystem Machine learning Rivers Salinity

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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