[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126826-en":3,"doc-seo-126826-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126826,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning","Discrepancies between simulation and real solid-state quantum devices limit optimization and scalability, with material-defect disorder playing a major role in the reality gap. A physics-aware machine learning framework combines a physical model, deep learning, Gaussian random fields, and Bayesian inference to infer a nanoscale device’s disorder potential from electron-transport data. The method is validated by checking predicted gate-voltage settings for a laterally defined AlGaAs/GaAs double-quantum-dot regime. ","Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning  \nD. L. Craig, 1 H. Moon,1 F. Fedele, 1 D. T. Lennon, 1 B. van Straaten, 1 F. Vigneau,1 L. C. Camenzind,2 D. M. Zumbühl,2  \nG. A. D. Briggs, 1 M. A. Osborne,3 D. Sejdinovic,4 and N. Ares3,*  \n1Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom 2Department of Physics, University of Basel, 4056 Basel, Switzerland  \n3Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom  \n4Department of Statistics, University of Oxford, 24-29 St Giles, Oxford OX1 3LB, United Kingdom  \n (Received 19 December 2021; revised 16 May 2023; accepted 29 September 2023; published 4 January 2024)  \nThe discrepancies between reality and simulation impede the optimization and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach enables us to infer the disorder potential of a nanoscale electronic device from electron-transport data. This inference is validated by verifying the algorithm’s predictions about the gatevoltage values required for a laterally defined quantum-dot device in AlGaAs/GaAs to produce current features corresponding to a double-quantum-dot regime.  \nDOI: 10.1103/PhysRevX.14.011001 Subject Areas: Computational Physics,  \nCondensed Matter Physics,  \nSemiconductor Physics  \nI. INTRODUCTION  \nDifferences between theory and experiment pervade all of science and are one of the driving forces of human discovery. Simulations often require fewer resources than real experiments but rarely capture the full complexity of a system, limiting their practical application. Narrowing the gap between a model and the real world is key for the control of complex systems using machine learning, especially when a machine learning model is trained on a simulation before being applied to real systems [1,2] . The reality gap is widened further when there are quantities which are not directly observable. Such unobservable quantities may be estimated through their influence on other characteristics of the system, for example, indirect observation of black holes [3], observation of the signature of Higgs boson decay [4], or machine learning estimation of human poses from behind walls [5] .  \nSolid-state quantum devices of nominally identical design will often display different characteristics. This variability hinders the scalability of otherwise promising qubit realizations, such as in the spin states of electrons  \n*[natalia.ares@eng.ox.ac.uk](natalia.ares@eng.ox.ac.uk)  \nPublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \nconfined in electrostatically defined quantum dots [6–8] . Different devices exhibit different electron-transport features for identical gate-voltage values. This variability is even observed in the same device after being exposed to thermal cycling [9] . In particular, electrostatic disorder induced by randomly located donor ions can be a significant source of variability in delta-doped semiconductor quantum-dot devices [10,11] . Recent theoretical work has used deep learning to reconstruct disorder potentials from partial local density-of-states calculations [12] . Confinement potentials of individual quantum dots have been probed using in-plane magnetic fields [13], but there has been no quantitative experimental study of the disorder present in these devices beyond the observation of its effects [14] .  \nBeing able to observe disorder potentials and provide a quantitativ","cbCairxfx699C8id","https://ap.wps.com/l/cbCairxfx699C8id","pdf",2365859,1,16,"English","en",105,"# Introduction\n## Reality gap between theory and experiment\n## Disorder as a source of variability in solid-state quantum devices\n## Physics-aware machine learning for disorder inference","[{\"question\":\"What causes the reality gap in solid-state quantum devices?\",\"answer\":\"A key cause is electrostatic disorder from randomly distributed material defects, which makes real devices deviate from idealized simulations.\"},{\"question\":\"How does physics-aware machine learning infer disorder potential?\",\"answer\":\"It combines a physical model with deep learning, Gaussian random fields, and Bayesian inference, using indirect electron-transport measurements to infer the hidden disorder potential.\"},{\"question\":\"How is the approach validated experimentally?\",\"answer\":\"Predicted gate-voltage values are checked against the settings needed for an AlGaAs/GaAs laterally defined double-quantum-dot device to show transport features consistent with the double-quantum-dot regime.\"}]","Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning | 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causes the reality gap in solid-state quantum devices?","Question",{"text":75,"@type":76},"A key cause is electrostatic disorder from randomly distributed material defects, which makes real devices deviate from idealized simulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does physics-aware machine learning infer disorder potential?",{"text":80,"@type":76},"It combines a physical model with deep learning, Gaussian random fields, and Bayesian inference, using indirect electron-transport measurements to infer the hidden disorder potential.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the approach validated experimentally?",{"text":84,"@type":76},"Predicted gate-voltage values are checked against the settings needed for an AlGaAs/GaAs laterally defined double-quantum-dot device to show transport features consistent with the double-quantum-dot 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