[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290779-105":59,"doc-detail-290779-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-majorana-nanowire-disorder-landscape","Machine learning Majorana nanowire disorder landscape","","We develop a practical machine-learning approach that determines the disorder landscape of Majorana nanowires from measured tunnel conductance. Training uses the conductance matrix and then inverts conductance data with different disorder parametrizations. The inversion is shown to be unique: any input tunnel conductance versus chemical potential and Zeeman energy can be inverted to recover the correct disorder. The method enables direct extraction of topological invariant and Majorana wave-function structure, supports optimization via unknown disorder, and yields an estimate of spin-orbit coupling for quantitative device verification.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-majorana-nanowire-disorder-landscape/290779/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-majorana-nanowire-disorder-landscape/290779.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the machine learning method determine the disorder landscape of Majorana nanowires?","Question",{"text":113,"@type":114},"It trains on the conductance matrix and then inverts measured tunnel conductance data using different disorder parametrizations to extract the underlying disorder details.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does the paper claim about the uniqueness of the inversion?",{"text":118,"@type":114},"The inversion is described as unique: the disorder producing the input tunnel conductance is recovered by the machine-learning procedure.",{"name":120,"@type":111,"acceptedAnswer":121},"What additional quantities can be obtained beyond the disorder landscape?",{"text":122,"@type":114},"The approach can be used to directly estimate the topological invariant and the Majorana wave-function structure, and it can also estimate the applicable spin-orbit coupling from the same scheme.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290779,1789642555,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","arXiv :2307 . 11068v1 [ cond-mat .mes-hall ] 20 Jul 2023  \nMachine learning Majorana nanowire disorder landscape  \nJacob R. Taylor, Jay D. Sau, and Sankar Das Sarma  \nCondensed Matter Theory Center and Joint Quantum Institute, Department of Physics, University of Maryland, College Park, Maryland 20742-4111 USA  \nWe develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding toa transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme.  \nIntroduction.—Hybrid superconductor-semiconductor (SC-SM) nanowire structures are the most extensively studied systems for creating laboratory Majorana zero modes (MZMs), which are localized non-Abelian excitations that can be used for creating a topological quantum computer. [1–3] . A recent experiment [1] has provided extensive tunnel conductance measurements to make the case for the existence of small and fragile topological regimes where MZMs should exist at the wire ends. Earlier experiments in such nanowires were strongly adversely affected by unintentional disorder in the system with the MZM-like conductance signatures arising from disorder-induced trivial Andreev bound states (and not from topological MZMs) . [2, 4–10] Although cleaner samples are used in [1], the disorder situation even for this latest experiment is not yet completely clarified, but the small fragile gaps reported in [1] indicate that random disorder is likely still playing a role. [11, 12] Disorder has thus emerged as the single most important physical mechanism suppressing topology, certainly in Majorana nanowires, but likely in most solid state topological platforms. Although the possible importance of disorder in suppressing MZM physics was pointed out early [13– 24], we still do not have any direct information about the disorder in actual samples, hindering progress in the field. Understanding and controlling unintentional (and thus, unknown) random disorder has become by far the most important problem in the search for MZMs in solid state platforms. [2] Progress toward the realization of topological MZMs necessitates an understanding of the underlying disorder leading to cleaner and better samples. [[2, 11, 12] . This leads to the other key problem in the field of identifying MZMs in a device based on its transport characterization. This is because, as already mentioned, the unknown disorder leads to transport signatures that are often misinterpreted as MZMs, which often exist in a very limited part of parameter space in moderately disordered devices [12, 17] . Ultimately, this motivates the other key challenge in the field, which is to identify when an SC-SM nanowire device has been tuned  \nto support MZMs.  \nIn the current work, we introduce an intuitively appealing Machine Learning (ML) approach for figuring out and understanding the disorder landscape in Majorana nanowires using the tunnel conductance data, which are the standard measurements for Majorana nanowires carried out in every experiment (and simulated in the corresponding theories) . The disorder potential toge","cbCaiclvzLPWQYxh","https://ap.wps.com/l/cbCaiclvzLPWQYxh","pdf",26794642,13,"English","# Introduction\n# Machine Learning Approach and Uniqueness\n# Implications for Topological Invariants and Parameters","[{\"question\":\"How does the machine learning method determine the disorder landscape of Majorana nanowires?\",\"answer\":\"It trains on the conductance matrix and then inverts measured tunnel conductance data using different disorder parametrizations to extract the underlying disorder details.\"},{\"question\":\"What does the paper claim about the uniqueness of the inversion?\",\"answer\":\"The inversion is described as unique: the disorder producing the input tunnel conductance is recovered by the machine-learning procedure.\"},{\"question\":\"What additional quantities can be obtained beyond the disorder landscape?\",\"answer\":\"The approach can be used to directly estimate the topological invariant and the Majorana wave-function structure, and it can also estimate the applicable spin-orbit coupling from the same scheme.\"}]","Machine learning Majorana nanowire disorder landscape | PDF",33]