Nature Reviews Physics最新文献_第8页

A brief history of the ‘physicist’ “物理学家”简史

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-17 DOI: 10.1038/s42254-025-00806-8

Iwan Rhys Morus

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Was the paranormal good for physics? 超自然现象对物理学有好处吗？

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-17 DOI: 10.1038/s42254-025-00805-9

Richard Noakes

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Making the International Year of Quantum Science and Technology happen 举办国际量子科学技术年

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-16 DOI: 10.1038/s42254-025-00807-7

Nina Meinzer, Yanne K. Chembo, Joseph J. Niemela

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Bringing quantum physics to new generations 将量子物理带给新一代

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-08 DOI: 10.1038/s42254-024-00797-y

Ankita Anirban, John Gribbin

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A year to celebrate quantum physics 这是庆祝量子物理学的一年

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-08 DOI: 10.1038/s42254-024-00800-6

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Benchmarking quantum computers 对标量子计算机

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-07 DOI: 10.1038/s42254-024-00796-z

Timothy Proctor, Kevin Young, Andrew D. Baczewski, Robin Blume-Kohout

{"title":"Benchmarking quantum computers","authors":"Timothy Proctor, Kevin Young, Andrew D. Baczewski, Robin Blume-Kohout","doi":"10.1038/s42254-024-00796-z","DOIUrl":"10.1038/s42254-024-00796-z","url":null,"abstract":"The rapid pace of development in quantum computing technology has sparked a proliferation of benchmarks to assess the performance of quantum computing hardware and software. However, not all benchmarks are of equal merit. Good ones empower scientists, engineers, programmers and users to understand the power of a computing system, whereas bad ones can misdirect research and inhibit progress. In this Perspective, we survey the science of quantum computer benchmarking. We discuss the role of benchmarks and benchmarking and how good benchmarks can drive and measure progress towards the long-term goal of useful quantum computations, known as quantum utility. We explain how different kinds of benchmark quantify the performance of different parts of a quantum computer, discuss existing benchmarks, examine recent trends in benchmarking, and highlight important open research questions in this field. Although quantum computers are still in their infancy, their computational power is growing rapidly. This Perspective surveys and critiques the known ways to benchmark quantum computer performance, highlighting new challenges anticipated on the road to utility-scale quantum computing.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 2","pages":"105-118"},"PeriodicalIF":44.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-driven learning for inverse problems in quantum chromodynamics 量子色动力学逆问题的物理驱动学习

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-06 DOI: 10.1038/s42254-024-00798-x

Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang, Kai Zhou

{"title":"Physics-driven learning for inverse problems in quantum chromodynamics","authors":"Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang, Kai Zhou","doi":"10.1038/s42254-024-00798-x","DOIUrl":"10.1038/s42254-024-00798-x","url":null,"abstract":"The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex observations. This is particularly relevant for quantum chromodynamics (QCD) — the theory of strong interactions — with its inherent challenges in interpreting observational data and demanding computational approaches. This Perspective highlights advances of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics and drawing connections to machine learning. Physics-driven learning can extract quantities from data more efficiently in a probabilistic framework because embedding priors can reduce the optimization effort. In the application of first-principles lattice QCD calculations and QCD physics of hadrons, neutron stars and heavy-ion collisions, we focus on learning physically relevant quantities, such as perfect actions, spectral functions, hadron interactions, equations of state and nuclear structure. We also emphasize the potential of physics-driven designs of generative models beyond QCD physics. Integrating physics priors into machine learning enhances efficiency, reduces data needs and yields reliable results. This Perspective explores physics-driven learning and inverse modelling of generative models to provide solutions for inverse problem in quantum chromodynamics.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 3","pages":"154-163"},"PeriodicalIF":44.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Roadmap on machine learning glassy dynamics 机器学习玻璃动力学的路线图

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-06 DOI: 10.1038/s42254-024-00791-4

Gerhard Jung, Rinske M. Alkemade, Victor Bapst, Daniele Coslovich, Laura Filion, François P. Landes, Andrea J. Liu, Francesco Saverio Pezzicoli, Hayato Shiba, Giovanni Volpe, Francesco Zamponi, Ludovic Berthier, Giulio Biroli

{"title":"Roadmap on machine learning glassy dynamics","authors":"Gerhard Jung, Rinske M. Alkemade, Victor Bapst, Daniele Coslovich, Laura Filion, François P. Landes, Andrea J. Liu, Francesco Saverio Pezzicoli, Hayato Shiba, Giovanni Volpe, Francesco Zamponi, Ludovic Berthier, Giulio Biroli","doi":"10.1038/s42254-024-00791-4","DOIUrl":"10.1038/s42254-024-00791-4","url":null,"abstract":"Unravelling the connections between microscopic structure, emergent physical properties and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. We explore the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present both successful ML applications and open problems for the future, such as transferability and interpretability of ML approaches. To foster a collaborative community effort, we also highlight the ‘GlassBench’ dataset, which provides simulation data and benchmarks for both 2D and 3D glass formers. We compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. Our goal is to provide guidelines for the development of ML techniques in systems displaying slow dynamics and inspire new directions to improve our theoretical understanding of glassy liquids. Slow heterogeneous dynamics and the absence of visible structural order make it difficult to numerically and theoretically investigate glass-forming materials. This Technical Review outlines the role that machine learning tools can have and identifies key challenges, possible approaches and appropriate benchmarks.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 2","pages":"91-104"},"PeriodicalIF":44.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forty years of the Ellis–Baldwin test 四十年的埃利斯-鲍德温测试

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-06 DOI: 10.1038/s42254-024-00803-3

Nathan Secrest, Sebastian von Hausegger, Mohamed Rameez, Roya Mohayaee, Subir Sarkar

引用次数: 0

Precision isotope-shift spectroscopy for new physics searches and nuclear insights 用于新的物理搜索和核洞察的精密同位素移谱

IF 44.8 1区物理与天体物理

Nature Reviews Physics Pub Date : 2025-01-06 DOI: 10.1038/s42254-024-00793-2

Julian C. Berengut, Cédric Delaunay

引用次数: 0