Pub Date : 2025-01-16DOI: 10.1038/s42254-025-00807-7
Nina Meinzer, Yanne K. Chembo, Joseph J. Niemela
The UN has proclaimed 2025 an international year to celebrate quantum science. Yanne Chembo and Joe Niemela, two of the physicists involved in the proposal, share the story of the approval process for this initiative. Yanne K. Chembo and Joe Niemela were part of the committee that put together the successful proposal for the International Year of Quantum Science and Technology. They recount their experience of the process and of working with the UN.
{"title":"Making the International Year of Quantum Science and Technology happen","authors":"Nina Meinzer, Yanne K. Chembo, Joseph J. Niemela","doi":"10.1038/s42254-025-00807-7","DOIUrl":"10.1038/s42254-025-00807-7","url":null,"abstract":"The UN has proclaimed 2025 an international year to celebrate quantum science. Yanne Chembo and Joe Niemela, two of the physicists involved in the proposal, share the story of the approval process for this initiative. Yanne K. Chembo and Joe Niemela were part of the committee that put together the successful proposal for the International Year of Quantum Science and Technology. They recount their experience of the process and of working with the UN.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 2","pages":"71-72"},"PeriodicalIF":44.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363089","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}
{"title":"Bringing quantum physics to new generations","authors":"Ankita Anirban, John Gribbin","doi":"10.1038/s42254-024-00797-y","DOIUrl":"10.1038/s42254-024-00797-y","url":null,"abstract":"John Gribbin, physicist and popular science writer, reflects on the long running, and evolving, fascination of quantum mechanics.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 1","pages":"4-5"},"PeriodicalIF":44.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939501","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}
Pub Date : 2025-01-08DOI: 10.1038/s42254-024-00800-6
The UN has declared 2025 the International Year of Quantum Science and Technology. We join the celebration with a series to explore how quantum science interacts with society.
{"title":"A year to celebrate quantum physics","authors":"","doi":"10.1038/s42254-024-00800-6","DOIUrl":"10.1038/s42254-024-00800-6","url":null,"abstract":"The UN has declared 2025 the International Year of Quantum Science and Technology. We join the celebration with a series to explore how quantum science interacts with society.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 1","pages":"1-1"},"PeriodicalIF":44.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42254-024-00800-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1038/s42254-024-00796-z
Timothy Proctor, Kevin Young, Andrew D. Baczewski, Robin Blume-Kohout
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.
{"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}
Pub Date : 2025-01-06DOI: 10.1038/s42254-024-00798-x
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang, Kai Zhou
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.
{"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}
Pub Date : 2025-01-06DOI: 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
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.
{"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}
Pub Date : 2025-01-06DOI: 10.1038/s42254-024-00803-3
Nathan Secrest, Sebastian von Hausegger, Mohamed Rameez, Roya Mohayaee, Subir Sarkar
Modern cosmology is built on the assumption that the Universe is homogeneous and isotropic on large scales — but this is challenged by results of the Ellis–Baldwin test that show an unexplained anomaly in the distribution of distant galaxies and quasars.
{"title":"Forty years of the Ellis–Baldwin test","authors":"Nathan Secrest, Sebastian von Hausegger, Mohamed Rameez, Roya Mohayaee, Subir Sarkar","doi":"10.1038/s42254-024-00803-3","DOIUrl":"10.1038/s42254-024-00803-3","url":null,"abstract":"Modern cosmology is built on the assumption that the Universe is homogeneous and isotropic on large scales — but this is challenged by results of the Ellis–Baldwin test that show an unexplained anomaly in the distribution of distant galaxies and quasars.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 2","pages":"68-70"},"PeriodicalIF":44.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363095","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}
Pub Date : 2025-01-06DOI: 10.1038/s42254-024-00793-2
Julian C. Berengut, Cédric Delaunay
Shifts in the frequency of atomic spectral lines between different isotopes can be used to reveal subtle changes in the properties of nuclei. With the advent of ultra-high-precision optical spectroscopy, it is also possible to use these isotope shifts to probe physics beyond the standard model, specifically whether a fifth force exists. This Perspective outlines the progress and challenges of these studies and provides an outlook on future possibilities. This Perspective explores how the frequency shifts of atomic spectral lines between isotopes can be effective probes of physics beyond the standard model, when measured to high precision using optical spectroscopy.
{"title":"Precision isotope-shift spectroscopy for new physics searches and nuclear insights","authors":"Julian C. Berengut, Cédric Delaunay","doi":"10.1038/s42254-024-00793-2","DOIUrl":"10.1038/s42254-024-00793-2","url":null,"abstract":"Shifts in the frequency of atomic spectral lines between different isotopes can be used to reveal subtle changes in the properties of nuclei. With the advent of ultra-high-precision optical spectroscopy, it is also possible to use these isotope shifts to probe physics beyond the standard model, specifically whether a fifth force exists. This Perspective outlines the progress and challenges of these studies and provides an outlook on future possibilities. This Perspective explores how the frequency shifts of atomic spectral lines between isotopes can be effective probes of physics beyond the standard model, when measured to high precision using optical spectroscopy.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 2","pages":"119-125"},"PeriodicalIF":44.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363099","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}
Pub Date : 2024-12-19DOI: 10.1038/s42254-024-00794-1
Gregory S. Boebinger, Andrey V. Chubukov, Ian R. Fisher, F. Malte Grosche, Peter J. Hirschfeld, Stephen R. Julian, Bernhard Keimer, Steven A. Kivelson, Andrew P. Mackenzie, Yoshiteru Maeno, Joseph Orenstein, Brad J. Ramshaw, Subir Sachdev, Joerg Schmalian, Matthias Vojta
{"title":"Hydride superconductivity is here to stay","authors":"Gregory S. Boebinger, Andrey V. Chubukov, Ian R. Fisher, F. Malte Grosche, Peter J. Hirschfeld, Stephen R. Julian, Bernhard Keimer, Steven A. Kivelson, Andrew P. Mackenzie, Yoshiteru Maeno, Joseph Orenstein, Brad J. Ramshaw, Subir Sachdev, Joerg Schmalian, Matthias Vojta","doi":"10.1038/s42254-024-00794-1","DOIUrl":"10.1038/s42254-024-00794-1","url":null,"abstract":"","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":"7 1","pages":"2-3"},"PeriodicalIF":44.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939481","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}
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