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Utilizing probabilistic entanglement between sensors in quantum networks

Physical Review applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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Effect of the Stark shift on the low-energy interference structure in strong-field ionization

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Physical Review A 2021年第5期103卷 053105-053105页

作者： Weifeng Yang Jie Li Wenbin Jia Hongdan Zhang Xiwang Liu Ming Zhu Xiaohong Song Jing Chen Research Center for Advanced Optics and Photoelectronics Department of Physics College of Science Shantou University Shantou Guangdong 515063 China Institute of Mathematics Shantou University Shantou Guangdong 515063 China MOE Key Laboratory of Intelligent Manufacturing Technology Shantou University Shantou Guangdong 515063 China HEDPS Center for Applied Physics and Technology Peking University Beijing 100084 China Institute of Applied Physics and Computational Mathematics P.O. Box 8009 Beijing 100088 China

An improved quantum trajectory Monte Carlo method including the Stark shift of the initial state, Coulomb potential, and multielectron polarization-induced dipole potential is adopted to revisit the origin of the low-energy interference structure in the photoelectron momentum distribution of the xenon atom subjected to an intense laser field, and resolve the different contributions of these three effects. We found that the Stark shift plays an essential role on the low-energy interference structure, which moves the ringlike constructive interference structure to the lower momentum region. The formation of the low-energy interference structure is a result of the combined effects of Stark shift, laser, and Coulomb fields, while the multielectron polarization mainly enhance the probability of the low energy photoelectron spectrum. Our finding provides insight into the electron dynamics of atoms and molecules when driven by the intense laser fields.

关键词： Atomic & molecular processes in external fields Multiphoton or tunneling ionization & excitation

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Effect of Viscosity on Stopping Power for a Charged Particle Moving above Two-Dimensional Electron Gas

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LASER AND PARTICLE BEAMS 2022年第2期2022卷

作者： Chen, Lei Wang, Yu Jia, Yuesong Yang, Xianjun Li, Chunzhi Yi, Lin Jiang, Wei Zhang, Ya School of Physics Huazhong University of Science and Technology Wuhan 430074 China Department of Physics Wuhan University of Technology Wuhan 430070 China Institute of Fluid Physics China Academy of Engineering Physics Mianyang 621900 China Institute of Applied Physics and Computational Mathematics Beijing 100094 China College of Mathematics and Physics Inner Mongolia University for the Nationalities Tongliao 028043 China

In two-dimensional (2D) electron systems, the viscous flow is dominant when electron-electron collisions occur more frequently than the impurity or phonon scattering. In this work, a quantum hydrodynamic model, considering viscosity, is proposed to investigate the interaction of a charged particle moving above the two-dimensional viscous electron gas. The stopping power, perturbed electron gas density, and the spatial distribution of the velocity vector field have been theoretically analyzed and numerically calculated. The calculation results show that viscosity affects the spatial distribution and amplitude of the velocity field. The stopping power, which is an essential quantity for describing the interactions of ions with the 2D electron gas, is calculated, indicating that the incident particle will suffer less energy loss due to the weakening of the dynamic electron polarization and induced electric field in 2D electron gas with the viscosity. The values of the stopping power may be more accurate after considering the effect of viscosity. Our results may open up new possibilities to control the interaction of ions with 2D electron gas in the surface of metal or semiconductor heterostructure by variation of the viscosity.

关键词： Viscosity

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Moment-based metrics for molecules computable from cryogenic electron microscopy images

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Biological imaging 2024年 4卷 e3页

作者： Andy Zhang Oscar Mickelin Joe Kileel Eric J Verbeke Nicholas F Marshall Marc Aurèle Gilles Amit Singer Program in Applied and Computational Mathematics Princeton University Princeton NJ USA. Department of Mathematics and Oden Institute University of Texas at Austin Austin TX USA. Department of Mathematics Oregon State University Corvallis OR USA. Department of Mathematics Princeton University Princeton NJ USA.

Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam's method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.

关键词： Kam’s method alignment-free metric method of moments protein structural similarity rotationally invariant distance structural search

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Microscopic theory for the pair correlation function of liquidlike colloidal suspensions under shear flow

arXiv

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arXiv 2022年

作者： Banetta, Luca Leone, Francesco Anzivino, Carmine Murillo, Michael S. Zaccone, Alessio Department of Applied Science and Technology Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy Department of Physics 'A. Pontremoli' University of Milan via Celoria 16 Milan20133 Italy Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States

We present a theoretical framework to investigate the microscopic structure of concentrated hardsphere colloidal suspensions under strong shear flows by fully taking into account the boundary-layer structure of convective diffusion. We solve the pair Smoluchowski equation with shear separately in the compressing and extensional sectors of the solid angle, by means of matched asymptotics. A proper, albeit approximate, treatment of the hydrodynamic interactions in the different sectors allows us to construct a potential of mean force containing the effect of the flow field on pair correlations. We insert the obtained pair potential in the Percus-Yevick relation and use the latter as a closure to solve the Ornstein-Zernike integral equation. For a wide range of either the packing fraction η and the Péclet (Pe) number, we compute the pair correlation function and extract scaling laws for its value at contact. For all the considered value of Pe, we observe a very good agreement between theoretical findings and numerical results from literature, up to rather large values of η. The theory predicts a consistent enhancement of the structure factor S(k) at k → 0, upon increasing the Pe number. We argue this behaviour may signal the onset of a phase transition from the isotropic phase to a non-uniform one, induced by the external shear flow. Copyright © 2022, The Authors. All rights reserved.

关键词： Shear flow

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Digital Quantum Simulation of Cavity Quantum Electrodynamics: Insights from Superconducting and Trapped Ion Quantum Testbeds

arXiv

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arXiv 2024年

作者： Rubin, Alex H. Marinelli, Brian Norman, Victoria A. Rizvi, Zainab Burch, Ashlyn D. Naik, Ravi K. Kreikebaum, John Mark Chow, Matthew N.H. Lobser, Daniel S. Revelle, Melissa C. Yale, Christopher G. Ivory, Megan Santiago, David I. Spitzer, Christopher Krstic-Marinkovic, Marina Clark, Susan M. Siddiqi, Irfan Radulaski, Marina Department of Electrical and Computer Engineering University of California DavisCA95616 United States Department of Physics University of California DavisCA95616 United States Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Computer Science University of California DavisCA95616 United States Sandia National Laboratories AlbuquerqueNM87123 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institute for Theoretical Physics ETH Zurich Zurich8093 Switzerland

We explore the potential for hybrid development of quantum hardware where currently available quantum computers simulate open Cavity Quantum Electrodynamical (CQED) systems for applications in optical quantum communication, simulation and computing. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis-Cummings model containing N atoms coupled to a lossy cavity. We report the results of executing this algorithm on two noisy intermediate-scale quantum computers: a superconducting processor and a trapped ion processor, to simulate the population dynamics of an open CQED system featuring N = 3 atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform, obtaining results which agree closely with the exact solution of the system. These results can be used as a recipe for efficient and platform-specific quantum simulation of cavity-emitter systems on contemporary and future quantum computers. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Equilibrium States Corresponding to Targeted Hyperuniform Nonequilibrium Pair Statistics

arXiv

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arXiv 2022年

作者： Wang, Haina Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute of Materials Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States

The Zhang-Torquato conjecture [Phys. Rev. E 101, 032124 (2020)] states that any realizable pair correlation function g2(r) or structure factor S(k) of a translationally invariant nonequilibrium system can be attained by an equilibrium ensemble involving only (up to) effective two-body interactions. To further test and study this conjecture, we consider two singular nonequilibrium models of recent interest that also have the exotic hyperuniformity property: a 2D "perfect glass" and a 3D critical absorbing-state model. We find that each nonequilibrium target can be achieved accurately by equilibrium states with effective one- and two-body potentials, lending further support to the conjecture. To characterize the structural degeneracy of such a nonequilibrium-equilibrium correspondence, we compute higher-order statistics for both models, as well as those for a hyperuniform 3D uniformly randomized lattice (URL), whose higher-order statistics can be very precisely ascertained. Interestingly, we find that the differences in the higher-order statistics between nonequilibrium and equilibrium systems with matching pair statistics, as measured by the "hole" probability distribution, provides measures of the degree to which a system is out of equilibrium. We show that all three systems studied possess the bounded-hole property, and that holes near the maximum hole size in the URL are much rarer than those in the underlying simple cubic lattice. Remarkably, upon quenching, the effective potentials for all three systems possess local energy minima (i.e., inherent structures) with stronger forms of hyperuniformity compared to their target counterparts. Our methods are expected to facilitate the self-assembly of tunable hyperuniform soft-matter systems. © 2022, CC BY.

关键词： Higher order statistics

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Correlations between student connectivity and academic performance: a pandemic follow-up

arXiv

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arXiv 2022年

作者： Crossette, Nathan Carr, Lincoln D. Wilcox, Bethany R. Department of Physics University of Colorado Boulder BoulderCO80309 United States Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Social network analysis (SNA) has been gaining traction as a technique for quantitatively studying student collaboration. We analyze networks, constructed from student self-reports of collaboration on homework assignments, in two courses from the University of Colorado Boulder and one course from the Colorado School of Mines. All three courses occurred during the COVID-19 pandemic, which allows for a comparison between the course at the Colorado School of Mines (in a fully remote format) with results from a previous pre-pandemic study of student collaboration at the Colorado School of Mines (in a hybrid format). We compute nodal centrality measures and calculate the correlation between student centrality and performance. Results varied widely between each of the courses studied. The course at the Colorado School of Mines had strong correlations between many centrality measures and performance which matched the patterns seen in the pre-pandemic study. The courses at the University of Colorado Boulder showed weaker correlations, and one course showed nearly no correlations at all between students' connectivity to their classmates and their performance. Taken together, the results from the trio of courses indicate that the context and environment in which the course is situated play a more important role in fostering a correlation between student collaboration and course performance than the format (remote, hybrid, in-person) of the course. Additionally, we conducted a short study on the effect that missing nodes may have on the correlations calculated from the measured networks. This investigation showed that missing nodes tend to shift correlations towards zero, providing evidence that the statistically significant correlations measured in our networks are not spurious. © 2022, CC BY.

关键词： Students

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Recent Methodological Advances in Federated Learning for Healthcare

arXiv

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arXiv 2023年

作者： Zhang, Fan Kreuter, Daniel Chen, Yichen Dittmer, Sören Tull, Samuel Shadbahr, Tolou Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Gleadall, Nicholas Roberts, Michael Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom Department of Haematology University of Cambridge Cambridge United Kingdom

For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the pooling of data. Healthcare data has many simultaneous challenges which require new methodologies to address, such as highly-siloed data, class imbalance, missing data, distribution shifts and non-standardised variables. Federated learning adds significant methodological complexity to conventional centralised machine learning, requiring distributed optimisation, communication between nodes, aggregation of models and redistribution of models. In this systematic review, we consider all papers on Scopus that were published between January 2015 and February 2023 and which describe new federated learning methodologies for addressing challenges with healthcare data. We performed a detailed review of the 89 papers which fulfilled these criteria. Significant systemic issues were identified throughout the literature which compromise the methodologies in many of the papers reviewed. We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare. © 2023, CC BY.

关键词： Paper

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