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arXiv 2025年

作者： Contardo, Gabriella Trotta, Roberto Gioia, Serafina Di Hogg, David W. Villaescusa-Navarro, Francisco Center for Astrophysics and Cosmology University of Nova Gorica Ajdovščina Slovenia Trieste Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INAF – Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34143 Italy Trieste Italy Center for Cosmology and Particle Physics Department of Physics New York University New YorkNY United States Max-Planck-Institut für Astronomie Heidelberg Germany Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Astrophysical Sciences Peyton Hall Princeton University NJ08544-0010 United States

Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the universe, Ωm, from the features of a single galaxy. We investigate the statistical properties of two of these simulation suites, IllustrisTNG and ASTRID, confirming that Ωm induces a strong displacement on the distribution of galaxy features. We also observe that most other parameters have little to no effect on the distribution, except for the stellar-feedback parameter ASN1, which introduces some near-degeneracies that can be broken with specific features. These two properties explain the predictability of Ωm. We use Optimal Transport to further measure the effect of parameters on the distribution of galaxy properties, which is found to be consistent with physical expectations. However, we observe discrepancies between the two simulation suites, both in the effect of Ωm on the galaxy properties and in the distributions themselves at identical parameter values. Thus, although Ωm’s signature can be easily detected within a given simulation suite using just a single galaxy, applying this result to real observational data may prove significantly more challenging. © 2025, CC BY.

关键词： Galaxies

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Modeling Neural Networks Training Process with Markov Decision Process

Modeling Neural Networks Training Process with Markov Decisi...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yantao Bai Wanwei Liu Xinjun Mao Zhen Liang Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Institute for Quantum Information &#x0026 National University of Defense Technology Changsha China

ISBN: (纸本)9781665421874

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.

关键词： Training Machine learning algorithms Computational modeling Neural networks Machine learning Markov processes Controllability

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Variational quantum process tomography

arXiv

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arXiv 2021年

作者： Xue, Shichuan Liu, Yong Wang, Yang Zhu, Pingyu Guo, Chu Wu, Junjie Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China College of Information and Communication National University of Defense Technology Xi'an710006 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to 8 qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least 2 orders of magnitude less than required by the standard quantum process tomography. Copyright © 2021, The Authors. All rights reserved.

关键词： Tomography

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Variational tensor network operator

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Physical Review Research 2022年第4期4卷 043153-043153页

作者： Yu-Hsueh Chen Ke Hsu Wei-Lin Tu Hyun-Yong Lee Ying-Jer Kao Center for Theoretical Physics and Department of Physics National Taiwan University Taipei 10607 Taiwan Division of Display and Semiconductor Physics Korea University Sejong 30019 Korea Department of Applied Physics Graduate School Korea University Sejong 30019 Korea Interdisciplinary Program in E·ICT-Culture-Sports Convergence Korea University Sejong 30019 Korea Center for Quantum Science and Technology National Taiwan University Taipei 10607 Taiwan National Center for High-Performance Computing Hsinchu City 30076 Taiwan

We propose a simple and generic construction of the variational tensor network operators to study the quantum spin systems by the synergy of ideas from the imaginary-time evolution and the variational optimization of trial wave functions. By applying these operators to simple initial states, accurate variational ground-state wave functions with extremely few parameters can be obtained. Furthermore, the framework can be applied to spontaneously study symmetry-breaking, symmetry-protected topological, and intrinsic topologically ordered phases, and we show that symmetries of the local tensors associated with these phases can emerge directly after the optimization without any gauge fixing. This provides a universal way to identify quantum phase transitions without prior knowledge of the system.

关键词： Magnetism Topological order quantum many-body systems Tensor network methods

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Numerical Study of Fluid-Structure Interaction Dynamics under high-explosive Detonation on Massively Parallel Computers

Numerical Study of Fluid-Structure Interaction Dynamics unde...

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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Sen Zhang Xiao-Wei Guo Chao Li Yi Liu Ran Zhao Canqun Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan P.R. China

ISBN: (纸本)9781728176505

Fluid-structure interaction dynamics under high-explosie detonation is widely used in engineering application, and its numerical simulation is also challenging due to the violent reaction of the blast. Based on the open source software and coupling library, we developed a solver to support the numerical simulation of fluid-structure interaction dynamics under high-explosie detonation, and carried out a case analysis. In the test case, the motion process of perpendicular elastic flap under high-explosive detonation is selected and the simulation time is 5ms. The transient results of the whole simulation process are given. We also tested the case in parallel, using two fluid meshes, that is, 972,000 cells (Mesh I) and 2,405,000 cells (Mesh II), and the scale of structure mesh is 2,000 cells. The results show that in the case of Mesh I, the maximum speedup ratio is 5.47 and the efficiency is 15.71% when the number of processes is 32. In the case of Mesh II, the maximum speedup ratio is 10.91 and the efficiency is 17.05% when the number of processes is 64.

关键词： Couplings Fluid dynamics Scalability Dynamics Numerical simulation Libraries Numerical models

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Revealing Ultrafast Phonon Mediated Inter-Valley Scattering through Transient Absorption and high Harmonic Spectroscopies

arXiv

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arXiv 2023年

作者： Lively, Kevin Sato, Shunsuke A. Albareda, Guillermo Rubio, Angel Kelly, Aaron Max Planck Institute for the Structure and Dynamics of Matter Luruper Chaussee 149 Hamburg22761 Germany Institute for Software Technology Department of High-Performance Computing Rathausallee 12 Sankt Augustin53757 Germany Center for Computational Sciences University of Tsukuba Ibaraki Tsukuba305-8577 Japan Ideaded Carrer de la Tecnologia 35 Viladecans Barcelona08840 Spain Center for Computational Quantum Physics [CCQ Flatiron Institute New YorkNY10010 United States Hamburg Center for Ultrafast Imaging Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany

Processes involving ultrafast laser driven electron-phonon dynamics play a fundamental role in the response of quantum systems in a growing number of situations of interest, as evidenced by phenomena such as strongly driven phase transitions and light driven engineering of material properties. To show how these processes can be captured from a computational perspective, we simulate the transient absorption spectra and high harmonic generation signals associated with valley selective excitation and intra-band charge carrier relaxation in monolayer hexagonal boron nitride. We show that the multi-trajectory Ehrenfest dynamics approach, implemented in combination with real-time time-dependent density functional theory and tight-binding models, offers a simple, accurate and efficient method to study ultrafast electron-phonon coupled phenomena in solids under diverse pump-probe regimes which can be easily incorporated into the majority of real-time ab initio software packages. © 2023, CC BY-NC-SA.

关键词： Ultrafast lasers

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Correction to:21cmemu: an emulator of21cmfastsummary observables

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Monthly Notices of the Royal Astronomical Society 2024年第1期533卷 1045–1047页

作者： Breitman, Daniela Mesinger, Andrei Murray, Steven G Prelogović, David Qin, Yuxiang Trotta, Roberto Scuola Normale Superiore (SNS) Piazza dei Cavalieri 7 I-56125 Pisa PI Italy Centro Nazionale ‘High Performance Computer Big Data and Quantum Computing’ School of Earth and Space Exploration Arizona State University Tempe AZ 85287-1404 USA School of Physics University of Melbourne Parkville VIC 3010 Australia ARC Centre of Excellence for All-Sky Astrophysics in 3 Dimensions (ASTRO 3D) Scuola Internazionale Superiore di Studi Avanzati (SISSA) Via Bonomea 265 I-34136 Trieste Italy Imperial Centre for Inference and Cosmology (ICIC) Imperial College Blackett Laboratory Prince Consort Road London SW7 2AZ UK

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Security boundaries of an optical power limiter for protecting quantum key distribution systems

arXiv

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arXiv 2023年

作者： Peng, Qingquan Gao, Binwu Zaitsev, Konstantin Wang, Dongyang Ding, Jiangfang Liu, Yingwen Liao, Qin Guo, Ying Huang, Anqi Wu, Junjie 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 Automation Central South University Changsha410083 China China Greatwall Research Institute China Greatwall Technology Group Co. Ltd. Shenzhen518057 China Russian Quantum Center Skolkovo Moscow121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Vigo Quantum Communication Center University of Vigo Vigo Spain Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo Spain atlanTTic Research Center University of Vigo Vigo Spain College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

Unauthorized light injection has always been a vital threat to the practical security of a quantum key distribution (QKD) system. An optical power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-injection attacks shall be tested to clarify the security boundary before being widely deployed. To investigate the OPL’s security boundary in quantum cryptography, we comprehensively test and analyse the behavior of OPL under continuous-wave (c.w.) light-injection attacks and pulse illumination attacks with pulses’ repetition rate at 0.5-Hz, 40-MHz, and 1-GHz. The testing results illuminate the security boundary of the OPL, which allows one to properly employ the OPL in the use cases. The methodology of testing and analysis proposed here is applicable to other power-limitation components in a QKD system. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulse repetition rate

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quantum generative training using rényi divergences

arXiv

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arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： quantum computers

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Electron–phonon coupling in topological insulator Bi_2Se_3 thin films with different substrates

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Chinese Optics Letters 2019年第2期17卷 21-24页

作者： Tian Jiang Runlin Miao Jie Zhao Zhongjie Xu Tong Zhou Ke Wei Jie You Xin Zheng Zhenyu Wang Xiang’ai Cheng College of Advanced Interdisciplinary Studies National University of Defense Technology State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Interdisciplinary Center of Quantum Information National University of Defense Technology National Institute of Defense Technology Innovation Academy of Military Sciences PLA China

Broadband transient reflectivity traces were measured for Bi_2 Se_3 thin films with various substrates via a 400 nm pump–white-light-probe setup. We have verified the existence of a second Dirac surface state in Bi_2 Se_3 and qualitatively located it by properly analyzing the traces acquired at different probe wavelengths. Referring to the band structure of Bi_2 Se_3, the relaxation mechanisms for photo-excited electrons with different energies are also revealed and studied. Our results show a second rise of the transient reflection signal at the time scale of several picoseconds. The types of substrate can also significantly affect the dynamics of the rising signal. This phenomenon is attributed to the effect of lattice heating and coherent phonon processes. The mechanism study in this work will benefit the fabrication of high-performance photonic devices based on topological insulators.

关键词： phonon coupling insulator films

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