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Symmetry-enforced ideal lanternlike phonons in the ternary nitride Li6WN4

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Physical Review B 2021年第4期104卷 L041104-L041104页

作者： Xiaotian Wang Feng Zhou Tie Yang Minquan Kuang Zhi-Ming Yu Gang Zhang School of Physical Science and Technology Southwest University Chongqing 400715 China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore

Topological physics of phonon spectra has been attracting great interest. Using the first-principle calculations and symmetry analysis, we show the realistic ternary nitride Li6WN4 features ideal (nearly flat) nodal-surface and nodal-line structures in its phonon spectra. These nodal degeneracies are shaped like lanterns, and their existence is guaranteed by nonsymmorphic symmetry. The corresponding topological phonon surface state covers exactly half the surface Brillouin zone and can thereby be distinguished from those of conventional nodal-line and nodal-surface semimetals. Our study enriches the classification of topological quantum phases, predicts ideal material candidates, and provides a good platform for investigating the interaction between nodal-line and nodal-surface phonons.

关键词： First-principles calculations Phonons Topological materials

Entanglement-Induced Barren Plateaus

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PRX quantum 2021年第4期2卷 040316-040316页

作者： Carlos Ortiz Marrero Mária Kieferová Nathan Wiebe Data Sciences and Analytics Group Pacific Northwest National Laboratory Richland Washington 99354 USA Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney New South Wales 2007 Australia Department of Computer Science University of Toronto Ontario M5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory Richland Washington 99354 USA

We argue that an excess in entanglement between the visible and hidden units in a quantum neural network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume law in the entanglement entropy will give rise to models that are not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden subsystem with high probability. We show how this can cause both gradient-descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden and/or visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus.

关键词： Machine learning quantum algorithms

Variational learning of integrated quantum photonic circuits

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arXiv 2024年

作者： Zhang, Hui Yang, Chengran Mok, Wai-Keong Wan, Lingxiao Cai, Hong Li, Qiang Gao, Feng Luo, Xianshu Lo, Guo-Qiang Chin, Lip Ket Shi, Yuzhi Thompson, Jayne Gu, Mile Liu, Ai Qun The Hong Kong Polytechnic University Hong Kong Nanyang Technological University Singapore Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117543 Singapore California Institute of Technology PasadenaCA91125 United States Singapore Advanced Micro Foundry 11 Science Park Rd Singapore Department of Electrical Engineering City University of Hong Kong Kowloon Tong Hong Kong Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China Institute of High Performance Computing Agency for Science Technology and Research 138632 Singapore Complexity Institute Nanyang Technological University Singapore Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University Singapore

Integrated photonic circuits play a crucial role in implementing quantum information processing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, we present a variational learning approach for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic elements into the training process. The complicated circuit is treated as a single nonlinear logical operator, and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control, we adjust and optimize the internal parameters of the chip in real time for task-specific cost functions. We utilize a simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate to illustrate how our proposed approach works, and then apply the approach in the first demonstration of quantum stochastic simulation using integrated photonics. © 2024, CC BY.

关键词： Cost functions

Preparing a commercial quantum key distribution system for certification against implementation loopholes

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arXiv 2023年

作者： Makarov, Vadim Abrikosov, Alexey Chaiwongkhot, Poompong Fedorov, Aleksey K. Huang, Anqi Kiktenko, Evgeny Petrov, Mikhail Ponosova, Anastasiya Ruzhitskaya, Daria Tayduganov, Andrey Trefilov, Daniil Zaitsev, Konstantin Russian Quantum Center Skolkovo Moscow121205 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Bangkok10110 Thailand QRate Skolkovo Moscow143026 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Steklov Mathematical Institute Russian Academy of Sciences Moscow119991 Russia atlanTTic Research Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain National Research University Higher School of Economics Moscow101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarisation encoding, qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and post-processing. We analyse the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure. Copyright © 2023, The Authors. All rights reserved.

关键词： quantum cryptography

Robust second-order correlation of twin parametric beams generated by amplified spontaneous parametric down-conversion

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Chinese Optics Letters 2020年第12期18卷 63-67页

作者： Kunpeng Jia Xiaohan Wang Xinjie LV Ping Xu Zhenlin Wang ZhiWei Wuang Gang Zhao Yan-Xiao Gong Zhenda Xie Shining Zhu National Laboratory of Solid State Microstructures School of PhysicsSchool of Electronic Science and EngineeringCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Institute for Quantum Information and State Key Laboratory of High Performance Computing.College of Computing National University of Defense TechnologyChangsha 410073China Mesoscopic Optics and Quantum Electronics Laboratory University of California Los AngelesCciliforniaCA 90095USA

We report an observation of the second-order correlation between twin beams generated by amplified spontaneous parametric down-conversion operating above threshold with kilowatt-level peak power, from a periodically poled Li Ta O3 crystal via a single-pass scheme. Photocurrent correlation was measured because of the bright photon streams, with raw visibility of 37.9% or 97.3% after electronic filtering. As expected in our theory, this correlation is robust and insensitive to parametric gain and detection loss, enabling important applications in optical communications, precision measurement, and nonlocal imaging.

关键词： amplified spontaneous parametric down-con version robust second-order correlation high-gain twin beams

Tensor network based finite-size scaling for two-dimensional Ising model

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Physical Review B 2023年第20期107卷 205123-205123页

作者： Ching-Yu Huang Sing-Hong Chan Ying-Jer Kao Pochung Chen Department of Applied Physics Tunghai University Taichung 40704 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan Department of Physics National Taiwan University Taipei 10607 Taiwan Center for Quantum Science and Technology National Taiwan University Taipei 10607 Taiwan National Center for High-Performance Computing Hsinchu City 30076 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Frontier Center for Theory and Computation National Tsing Hua University Hsinchu 30013 Taiwan

We propose a scheme to perform tensor network based finite-size scaling analysis for two-dimensional classical models. In the tensor network representation of the partition function, we use higher-order tensor renormalization group (HOTRG) method to coarse grain the weight tensor. The renormalized tensor is then used to construct the approximated transfer matrix of an infinite strip of finite width. By diagonalizing the transfer matrix we obtain the correlation length, the magnetization, and the energy density, which are used in finite-size scaling analysis to determine the critical temperature and the critical exponents. As a benchmark we study the two-dimensional classical Ising model. We show that the critical temperature and the critical exponents can be accurately determined. With HOTRG bond dimension D=70, the absolute errors of the critical temperature Tc and the critical exponent ν, β are at the order of 10−7,10−5, 10−4, respectively. Furthermore, the results can be systematically improved by increasing the bond dimension of the HOTRG method. Finally, we study the length scale induced by the finite cutoff in bond dimension and elucidate its physical meaning in this context.

关键词： Finite-size scaling Ising model Renormalization group Tensor network methods

Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914

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arXiv 2023年

作者： Crisostomi, Marco Dey, Kallol Barausse, Enrico Trotta, Roberto SISSA Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Maruthamala PO Vithura Kerala Thiruvananthapuram695551 India Physics Department Imperial College London Prince Consort Rd LondonSW7 2AZ United Kingdom Centro Nazionale "High Performance Computer Big Data and Quantum Computing" Italy

We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a simulation-based inference pipeline based on masked autoregressive flows. We obtain approximate marginal posterior distributions for the ringdown parameters, namely the mass, spin, and the amplitude and phases of the dominant mode and its first overtone. Thanks to the locally amortized nature of our method, we are able to calibrate our posteriors with injected simulations, producing posterior regions with guaranteed (i.e. exact) frequentist coverage of the true values. For GW150914, our calibrated posteriors provide only mild evidence (∼ 2σ) for the presence of an overtone, even if the ringdown is assumed to start at the peak of the amplitude. Copyright © 2023, The Authors. All rights reserved.

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on high performance Computers

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SSRN

SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

Tensor Network Based Finite-Size Scaling for Two-Dimensional Classical Models

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arXiv 2023年

作者： Huang, Ching-Yu Chan, Sing-Hong Kao, Ying-Jer Chen, Pochung Department of Applied Physics Tunghai University Taichung40704 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Department of Physics National Taiwan University Taipei10607 Taiwan Center for Quantum Science and Technology National Taiwan University Taipei10607 Taiwan National Center for High-Performance Computing Hsinchu City30076 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Frontier Center for Theory and Computation National Tsing Hua University Hsinchu30013 Taiwan

We propose a scheme to perform tensor network based finite-size scaling analysis for two-dimensional classical models. In the tensor network representation of the partition function, we use higher-order tensor renormalization group (HOTRG) method to coarse grain the weight tensor. The renormalized tensor is then used to construct the approximated transfer matrix of an infinite strip of finite width. By diagonalizing the transfer matrix we obtain the correlation length, the magnetization, and the energy density which are used in finite-size scaling analysis to determine the critical temperature and the critical exponents. As a benchmark we study the two-dimensional classical Ising model. We show that the critical temperature and the critical exponents can be accurately determined. With HOTRG bond dimension D = 70, the absolute errors of the critical temperature Tc and the critical exponent ν, β are at the order of 10−7, 10−5, 10−4 respectively. Furthermore, the results can be systematically improved by increasing the bond dimension of the HOTRG method. Finally, we study the length scale induced by the finite cut-off in bond dimension and elucidate its physical meaning in this context. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors