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Wang Y, Liu J. A comprehensive review of quantum machine learning: from NISQ to fault tolerance. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2024; 87:116402. [PMID: 39321817 DOI: 10.1088/1361-6633/ad7f69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 09/25/2024] [Indexed: 09/27/2024]
Abstract
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
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Affiliation(s)
- Yunfei Wang
- Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, MD 20742, United States of America
- Maryland Center for Fundamental Physics, University of Maryland, College Park, MD 20742, United States of America
| | - Junyu Liu
- Department of Computer Science, The University of Pittsburgh, Pittsburgh, PA 15260, United States of America
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, United States of America
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, United States of America
- Kadanoff Center for Theoretical Physics, The University of Chicago, Chicago, IL 60637, United States of America
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2
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Fernández-Fernández G, Manzo C, Lewenstein M, Dauphin A, Muñoz-Gil G. Learning minimal representations of stochastic processes with variational autoencoders. Phys Rev E 2024; 110:L012102. [PMID: 39160967 DOI: 10.1103/physreve.110.l012102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 05/29/2024] [Indexed: 08/21/2024]
Abstract
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are, however, difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method builds upon an extended β-variational autoencoder architecture. By means of simulated data sets corresponding to paradigmatic diffusion models, we showcase its effectiveness in extracting the minimal relevant parameters that accurately describe these dynamics. Furthermore, the method enables the generation of new trajectories that faithfully replicate the expected stochastic behavior. Overall, our approach enables the autonomous discovery of unknown parameters describing stochastic processes, hence enhancing our comprehension of complex phenomena across various fields.
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Affiliation(s)
| | - Carlo Manzo
- Facultat de Ciències, Tecnologia i Enginyeries, Universitat de Vic-Universitat Central de Catalunya (UVic-UCC), C. de la Laura, 13, 08500 Vic, Spain
- Bioinformatics and Bioimaging, Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), 08500 Vic, Barcelona, Spain
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3
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Cui C, Horrocks W, Hao S, Guha S, Peyghambarian N, Zhuang Q, Zhang Z. Quantum receiver enhanced by adaptive learning. LIGHT, SCIENCE & APPLICATIONS 2022; 11:344. [PMID: 36481525 PMCID: PMC9731947 DOI: 10.1038/s41377-022-01039-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/08/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical receivers. To date, only a handful of quantum receivers have been constructed to tackle the problem of discriminating coherent states. Quantum receivers designed by analytical approaches, however, are incapable of effectively adapting to diverse environmental conditions, resulting in their quickly diminishing performance as the operational complexities increase. Here, we present a general architecture, dubbed the quantum receiver enhanced by adaptive learning, to adapt quantum receiver structures to diverse operational conditions. The adaptively learned quantum receiver is experimentally implemented in a hardware platform with record-high efficiency. Combining the architecture and the experimental advances, the error rate is reduced up to 40% over the standard quantum limit in two coherent-state encoding schemes.
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Affiliation(s)
- Chaohan Cui
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - William Horrocks
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Shuhong Hao
- Department of Materials Science and Engineering, University of Arizona, Tucson, AZ, 85721, USA
| | - Saikat Guha
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, 85721, USA
| | - Nasser Peyghambarian
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA
- Department of Materials Science and Engineering, University of Arizona, Tucson, AZ, 85721, USA
| | - Quntao Zhuang
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, 85721, USA
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zheshen Zhang
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, 85721, USA.
- Department of Materials Science and Engineering, University of Arizona, Tucson, AZ, 85721, USA.
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, 85721, USA.
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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4
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Krenn M, Ai Q, Barthel S, Carson N, Frei A, Frey NC, Friederich P, Gaudin T, Gayle AA, Jablonka KM, Lameiro RF, Lemm D, Lo A, Moosavi SM, Nápoles-Duarte JM, Nigam A, Pollice R, Rajan K, Schatzschneider U, Schwaller P, Skreta M, Smit B, Strieth-Kalthoff F, Sun C, Tom G, Falk von Rudorff G, Wang A, White AD, Young A, Yu R, Aspuru-Guzik A. SELFIES and the future of molecular string representations. PATTERNS (NEW YORK, N.Y.) 2022; 3:100588. [PMID: 36277819 PMCID: PMC9583042 DOI: 10.1016/j.patter.2022.100588] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
| | - Qianxiang Ai
- Department of Chemistry, Fordham University, The Bronx, NY, USA
| | - Senja Barthel
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nessa Carson
- Syngenta Jealott’s Hill International Research Centre, Bracknell, Berkshire, UK
| | - Angelo Frei
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, UK
| | - Nathan C. Frey
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Théophile Gaudin
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- IBM Research Europe, Zürich, Switzerland
| | | | - Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Rafael F. Lameiro
- Medicinal and Biological Chemistry Group, São Carlos Institute of Chemistry, University of São Paulo, São Paulo, Brazil
| | - Dominik Lemm
- Faculty of Physics, University of Vienna, Vienna, Austria
| | - Alston Lo
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Seyed Mohamad Moosavi
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | | | - AkshatKumar Nigam
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller Universität Jena, Jena, Germany
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Philippe Schwaller
- IBM Research Europe, Zürich, Switzerland
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marta Skreta
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Felix Strieth-Kalthoff
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Chong Sun
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Gary Tom
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | | | - Andrew Wang
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Solar Fuels Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - Adamo Young
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Materials Science, University of Toronto, Toronto, ON, Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, ON, Canada
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Krenn M, Pollice R, Guo SY, Aldeghi M, Cervera-Lierta A, Friederich P, dos Passos Gomes G, Häse F, Jinich A, Nigam A, Yao Z, Aspuru-Guzik A. On scientific understanding with artificial intelligence. NATURE REVIEWS. PHYSICS 2022; 4:761-769. [PMID: 36247217 PMCID: PMC9552145 DOI: 10.1038/s42254-022-00518-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 05/27/2023]
Abstract
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Si Yue Guo
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Alba Cervera-Lierta
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Gabriel dos Passos Gomes
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medical College, New York, USA
| | - AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Zhenpeng Yao
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, Ontario Canada
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Lollie MLJ, Mostafavi F, Bhusal N, Hong M, You C, León-Montiel RDJ, Magaña-Loaiza OS, Quiroz-Juárez MA. High-dimensional encryption in optical fibers using spatial modes of light and machine learning. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac7f1b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
The ability to engineer the spatial wavefunction of photons has enabled a variety of quantum protocols for communication, sensing, and information processing. These protocols exploit the high dimensionality of structured light enabling the encoding of multiple bits of information in a single photon, the measurement of small physical parameters, and the achievement of unprecedented levels of security in schemes for cryptography. Unfortunately, the potential of structured light has been restrained to free-space platforms in which the spatial profile of photons is preserved. Here, we make an important step forward to using structured light for fiber optical communication. We introduce a classical encryption protocol in which the propagation of high-dimensional spatial modes in multimode fibers is used as a natural mechanism for encryption. This provides a secure communication channel for data transmission. The information encoded in spatial modes is retrieved using artificial neural networks, which are trained from the intensity distributions of experimentally detected spatial modes. Our on-fiber communication platform allows us to use single spatial modes for information encoding as well as the high-dimensional superposition modes for bit-by-bit and byte-by-byte encoding respectively. This protocol enables one to recover messages and images with almost perfect accuracy. Our classical smart protocol for high-dimensional encryption in optical fibers provides a platform that can be adapted to address increased per-photon information capacity at the quantum level, while maintaining the fidelity of information transfer. This is key for quantum technologies relying on structured fields of light, particularly those that are challenged by free-space propagation.
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Flam-Shepherd D, Wu TC, Gu X, Cervera-Lierta A, Krenn M, Aspuru-Guzik A. Learning interpretable representations of entanglement in quantum optics experiments using deep generative models. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00493-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Eckstein M, Horodecki P. Probing the limits of quantum theory with quantum information at subnuclear scales. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2021.0806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Modern quantum engineering techniques enabled successful foundational tests of quantum mechanics. Yet, the universal validity of quantum postulates is an open question. Here we propose a new theoretical framework of Q-data tests, which recognizes the established validity of quantum theory, but allows for more general—‘post-quantum’—scenarios in certain physical regimes. It can accommodate a large class of models with modified quantum wave dynamics, correlations beyond entanglement or general probabilistic postulates. We discuss its experimental implementation suited to probe the nature of strong nuclear interactions. In contrast to the present accelerator experiments, it shifts the focus from high-luminosity beam physics to individual particle coherent control.
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Affiliation(s)
- Michał Eckstein
- Institute of Theoretical Physics, Jagiellonian University, ul. Łojasiewicza 11, 30–348 Kraków, Poland
- Copernicus Center for Interdisciplinary Studies, ul. Szczepańska 1/5, 31-011 Kraków, Poland
| | - Paweł Horodecki
- International Centre for Theory of Quantum Technologies, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
- Faculty of Applied Physics and Mathematics, National Quantum Information Centre, Gdańsk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdańsk, Poland
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Abstract
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.
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Xu S, McLeod AS, Chen X, Rizzo DJ, Jessen BS, Yao Z, Wang Z, Sun Z, Shabani S, Pasupathy AN, Millis AJ, Dean CR, Hone JC, Liu M, Basov DN. Deep Learning Analysis of Polaritonic Wave Images. ACS NANO 2021; 15:18182-18191. [PMID: 34714043 DOI: 10.1021/acsnano.1c07011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.
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Affiliation(s)
- Suheng Xu
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Alexander S McLeod
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Xinzhong Chen
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Daniel J Rizzo
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Bjarke S Jessen
- Department of Physics, Columbia University, New York, New York 10027, United States
- Department of Mechanical Engineering, Columbia University, New York, New York 10027, United States
| | - Ziheng Yao
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Zhicai Wang
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Zhiyuan Sun
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Sara Shabani
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Abhay N Pasupathy
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Andrew J Millis
- Department of Physics, Columbia University, New York, New York 10027, United States
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, United States
| | - Cory R Dean
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - James C Hone
- Department of Mechanical Engineering, Columbia University, New York, New York 10027, United States
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - D N Basov
- Department of Physics, Columbia University, New York, New York 10027, United States
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11
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Friederich P, Krenn M, Tamblyn I, Aspuru-Guzik A. Scientific intuition inspired by machine learning-generated hypotheses. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abda08] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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