检索结果-内蒙古大学图书馆

Efficient simulation of loop quantum gravity — a scalable linear-optical approach

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Cohen, Lior Brady, Anthony J. Huang, Zichang Liu, Hongguang Qu, Dongxue Dowling, Jonathan P. Han, Muxin Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Physics Center for Field Theory and Particle Physics Institute for Nano- electronic devices and Quantum computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Center for Quantum Computing Pengcheng Laboratory Shenzhen518066 China Department of Physics Florida Atlantic University 777 Glades Road Boca RatonFL33431 United States NYU ECNU Institute of Physics at NYU Shanghai 3663 Zhongshan Road North Shanghai200062 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China National Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi Koganei Tokyo184-8795 Japan Institut für Quantengravitation Universität Erlangen-Nürnberg Staudtstr. 7/B2 Erlangen91058 Germany

The problem of simulating complex quantum processes on classical computers gave rise to the field of quantum simulations. quantum simulators solve problems, such as boson sampling, where classical counterparts fail. In another field of physics, the unification of general relativity and quantum theory is one of the greatest challenges of our time. One leading approach is Loop quantum Gravity (LQG). Here, we connect these two fields and design a linear-optical simulator such that the evolution of the optical quantum gates simulates the spinfoam amplitudes of LQG. It has been shown that computing transition amplitudes in simple quantum field theories falls into the class BQP – which strongly suggests that computing transition amplitudes of LQG are classically intractable. Therefore, these amplitudes are efficiently computable with universal quantum computers which are, alas, possibly decades away. We propose here an alternative special-purpose linear-optical quantum computer, which can be implemented using current technologies. This machine is capable of efficiently computing these quantities. This work opens a new way to relate quantum gravity to quantum information and will expand our understanding of the theory. Copyright © 2020, The Authors. All rights reserved.

关键词： Relativity

Control flow in active inference systems

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Fields, Chris Fabrocini, Filippo Friston, Karl Glazebrook, James F. Hazan, Hananel Levin, Michael Marcianò, Antonino Allen Discovery Center Tufts University MedfordMA02155 United States College of Design and Innovation Tongji University 281 Fuxin Rd Shanghai200092 China Institute for Computing Applications "Mario Picone" Italy National Research Council Via dei Taurini 19 Rome00185 Italy Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom VERSES Research Lab Los AngelesCA90016 United States Department of Mathematics and Computer Science Eastern Illinois University CharlestonIL61920 United States Adjunct Faculty Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02115 United States Center for Field Theory and Particle Physics Department of Physics Fudan University Shanghai China Laboratori Nazionali di Frascati INFN Frascati Rome Italy INFN sezione Roma "Tor Vergata" RomeI-00133 Italy

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales. © 2023, CC BY.

关键词： Free energy

Necessary and sufficient conditions on measurements of quantum channels

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Burniston, John Grabowecky, Michael Scandolo, Carlo Maria Chiribella, Giulio Gour, Gilad Department of Mathematics & Statistics University of Calgary CalgaryAB Canada Institute for Quantum Science and Technology University of Calgary CalgaryAB Canada Institute for Quantum Computing University of Waterloo WaterlooON Canada Department of Computer Science University of Hong Kong Hong Kong Hong Kong Department of Computer Science University of Oxford Oxford United Kingdom

quantum supermaps are a higher-order generalization of quantum maps, taking quantum maps to quantum maps. It is known that any completely positive, trace non-increasing (CPTNI) map can be performed as part of a quantum measurement. By providing an explicit counterexample we show that, instead, not every quantum supermap sending a quantum channel to a CPTNI map can be realized in a measurement on quantum channels. We find that the supermaps that can be implemented in this way are exactly those transforming quantum channels into CPTNI maps even when tensored with the identity supermap. Copyright © 2019, The Authors. All rights reserved.

关键词： quantum entanglement

Efficient Verification of Hypergraph States

学校读者我要写书评

暂无评论

Physical Review Applied 2019年第5期12卷 054047-054047页

作者： Huangjun Zhu Masahito Hayashi Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China Institute for Theoretical Physics University of Cologne Cologne 50937 Germany Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

Graph states and hypergraph states are of wide interest in quantum information processing and foundational studies. Efficient verification of these states is a key to various applications. Here we propose a simple method for verifying hypergraph states, which requires only two distinct Pauli measurements for each party, yet its efficiency is comparable to the best strategy based on entangling measurements. For a given state, the overhead is bounded by the chromatic number and degree of the underlying hypergraph. Our protocol is dramatically more efficient than all previous protocols based on local measurements, including tomography and direct-fidelity estimation. It enables the verification of hypergraph states and genuine multipartite entanglement of thousands of qubits. The protocol can also be generalized to the adversarial scenario, while achieving almost the same efficiency. This merit is particularly appealing to demonstrating blind measurement-based quantum computation and quantum supremacy.

关键词： Entanglement detection Measurement-based quantum computing quantum benchmarking quantum information processing quantum networks quantum tomography Resource theories Graph theory

Improving unsupervised domain adaptation by reducing bi-level feature redundancy

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

How to hire secretaries with stochastic departures

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Kesselheim, Thomas Psomas, Alexandros Vardi, Shai Institute of Computer Science University of Bonn Bonn53115 Germany Simons Institute for the Theory of Computing BerkeleyCA United States Krannert School of Management Purdue University West LafayetteIN United States

We study a generalization of the secretary problem, where decisions do not have to be made immediately upon candidates' arrivals. After arriving, each candidate stays in the system for some (random) amount of time and then leaves, whereupon the algorithm has to decide irrevocably whether to select this candidate or not. The goal is to maximize the probability of selecting the best candidate overall. We assume that the arrival and waiting times are drawn from known distributions. Our first main result is a characterization of the optimal policy for this setting. We show that when deciding whether to select a candidate it suffices to know only the time and the number of candidates that have arrived so far. Furthermore, the policy is monotone non-decreasing in the number of candidates seen so far, and, under certain natural conditions, monotone nonincreasing in the time. Our second main result is proving that when the number of candidates is large, a single threshold policy is almost optimal. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

DECIDABILITY FOR STURMIAN WORDS

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Hieronymi, Philipp Ma, Dun Oei, Reed Schaeffer, Luke Schulz, Chris Shallit, Jeffrey Mathematisches Institut Universität Bonn Endenicher Allee 60 BonnD-53115 Germany Department of Computer Science and Engineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0404 United States Department of Mathematics University of Illinois at Urbana-Champaign 1409 West Green Street UrbanaIL61801 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Pure Mathematics 200 University Avenue West WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada

We show that the first-order theory of Sturmian words over Presburger arithmetic is decidable. Using a general adder recognizing addition in Ostrowski numeration systems by Baranwal, Schaeffer and Shallit, we prove that the first-order expansions of Presburger arithmetic by a single Sturmian word are uniformly ω-automatic, and then deduce the decidability of the theory of the class of such structures. Using an implementation of this decision algorithm called Pecan, we automatically reprove classical theorems about Sturmian words in seconds, and are able to obtain new results about antisquares and antipalindromes in characteristic Sturmian words. © 2021, CC BY.

关键词： Computability and decidability

Hierarchy of linear light cones with long-range interactions

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Tran, Minh C. Chen, Chi-Fang Ehrenberg, Adam Guo, Andrew Y. Deshpande, Abhinav Hong, Yifan Gong, Zhe-Xuan Gorshkov, Alexey V. Lucas, Andrew Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Physics California Institute of Technology PasadenaCA91125 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics Colorado School of Mines GoldenCO80401 United States National Institute of Standard and Technology BoulderCO80305 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80309 United States

In quantum many-body systems with local interactions, quantum information and entanglement cannot spread outside of a linear light cone, which expands at an emergent velocity analogous to the speed of light. Local operations at sufficiently separated spacetime points approximately commute—given a many-body state |ψ〉, Ox(t)Oy|ψ〉 ≈ OyOx(t)|ψ〉 with arbitrarily small errors—so long as |x − y| ≿ vt, where v is finite. Yet most non-relativistic physical systems realized in nature have long-range interactions: two degrees of freedom separated by a distance r interact with potential energy V (r) ∝ 1/rα. In systems with long-range interactions, we rigorously establish a hierarchy of linear light cones: at the same α, some quantum information processing tasks are constrained by a linear light cone while others are not. In one spatial dimension, this linear light cone exists for every many-body state |ψ〉 when α > 3 (Lieb-Robinson light cone);for a typical state |ψ〉 chosen uniformly at random from the Hilbert space when α > 5/2 (Frobenius light cone);for every state of a non-interacting system when α > 2 (free light cone). These bounds apply to time-dependent systems and are optimal up to subalgebraic improvements. Our theorems regarding the Lieb-Robinson and free light cones—and their tightness—also generalize to arbitrary dimensions. We discuss the implications of our bounds on the growth of connected correlators and of topological order, the clustering of correlations in gapped systems, and the digital simulation of systems with long-range interactions. In addition, we show that universal quantum state transfer, as well as many-body quantum chaos, are bounded by the Frobenius light cone, and therefore are poorly constrained by all Lieb-Robinson bounds. Copyright © 2020, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)