[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119882-en":3,"doc-seo-119882-105":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},119882,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements","Majorana zero modes (MZMs) are exotic quasiparticles enabling fault-tolerant topological quantum computation, with a key experimental hallmark being zero-bias peaks in tunneling differential conductance. Distinguishing MZMs from topologically trivial states that also generate spurious zero-bias peaks remains difficult. The work presents a machine-learning framework using zero-bias peak data, where tight-binding quantum transport simulations create training sets and persistent cohomology supports the classification feasibility. An XGBoost model achieves 85% accuracy for 1D and 94% for 2D data, and tests on prior experiments indicate varying likelihoods for MZM origin, offering a quantitative assessment route integrating experiment and computation.","arXiv :2310 . 18439v1 [ cond-mat .mes-hall ] 27 Oct 2023  \nMachine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements  \nMouyang Cheng 1,2,* , Ryotaro Okabe1,3 , Abhijatmedhi Chotrattanapituk 1,4 , and Mingda Li 1,5,**  \n1 Quantum Measurement Group, MIT, Cambridge, MA 02139, USA  \n2 School of Physics, Peking University, Beijing 100084, China  \n3 Department of Chemistry, MIT, Cambridge, MA 02139, USA  \n4 Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA  \n5 Department of Nuclear Science and Engineering, MIT, Cambridge, MA 02139, USA  \n* e-mail: [vipandyc@mit.edu](vipandyc@mit.edu)  \n** [e-mail: mingda@mit.edu](e-mail: mingda@mit.edu)  \nABSTRACT  \nMajorana zero modes (MZMs), emerging as exotic quasiparticles that carry non-Abelian statistics, hold great promise for achieving fault-tolerant topological quantum computation. A key signature of the presence of MZMs is the zero-bias peaks (ZBPs) from tunneling differential conductance. However, the identification of MZMs from ZBPs has faced tremendous challenges, due to the presence of topological trivial states that generate spurious ZBP signals. In this work, we introduce a machine-learning framework that can discern MZM from other signals using ZBP data. Quantum transport simulation from tight-binding models is used to generate the training data, while persistent cohomology analysis confirms the feasibility of classification via machine learning. In particular, even with added data noise, XGBoost classifier reaches 85% accuracy for 1D tunneling conductance data and 94% for 2D data incorporating Zeeman splitting. Tests on prior ZBP experiments show that some data are more likely to originate from MZM than others. Our model offers a quantitative approach to assess MZMs using ZBP data. Furthermore, our results shed light on the use of machine learning on exotic quantum systems with experimental-computational integration.  \nIntroduction  \nThe identification of quantum many-body phases from experimental observations is one of the central tasks in condensed matter physics 1–4. While symmetry-breaking phases can be detected unequivocally using local order parameters, topological phases of matter pose a more complex problem. Unlike the former, the topological phases cannot be characterized by local order parameters but instead carry global topological invariants5. As a result, detecting topological phases often requires an indirect measurement where topology can manifest, such as examining bulk excitations or specific boundary states6. Successful examples include the quantum anomalous Hall effect with insulating bulk and spin-polarized chiral edge states that can be probed by electrical transport7–9, or topological Weyl semimetals with bulk Weyl fermions and surface Fermi arcs using photoemission 10. In other cases, probing topology can become notably more challenging. In quantum spin liquids, for instance, bulk spinon excitations and edge Majorana fermions only leave subtle experimental evidence3, 11. An enhanced capability to detect topological phases of matter will greatly enrich our understanding of quantum phases and hold paramount importance for next-generation microelectronic and quantum computing applications.  \nAmong the exotic topological phases of matter, Majorana Zero Modes (MZM), characterized by the non-Abelian, Ising-type anyonic statistics, have captured significant research and industrial attention over the past decade. Thanks to their unique ability to store information nonlocally, and their intrinsic zero energy that guards against hybridization, MZMs are deemed a highly promising platform to realize fault-tolerant topological quantum computation 12–14. Theoretically, MZMs were first proposed in the Kitaev 1D chain model with p-wave superconductor, where pairs of MZMs can emerge at the ends of the chain 15. However, the evidence of p-wave superconductors has been elusive, with an unclear pathway to lift the d","cbCaiqDB0XbSmWwB","https://ap.wps.com/l/cbCaiqDB0XbSmWwB","pdf",13217350,1,24,"English","en",105,"# Abstract\n# Introduction\n## Topological phase detection challenges\n## Majorana zero modes and experimental signatures\n## Problem of trivial states producing ZBPs\n# Machine-learning pipeline","[{\"question\":\"What experimental signal is used to identify Majorana zero modes in this work?\",\"answer\":\"The method focuses on zero-bias peaks in tunneling differential conductance, which are measured in scanning tunneling spectroscopy and serve as the central input data.\"},{\"question\":\"Why is identifying MZMs from zero-bias peaks challenging?\",\"answer\":\"Topologically trivial states can also produce spurious zero-bias peak signals, making zero-bias peaks an ambiguous indicator without additional discrimination.\"},{\"question\":\"How does the proposed approach train and validate the classifier?\",\"answer\":\"Training data are generated via quantum transport simulations from tight-binding models, and persistent cohomology analysis is used to confirm the feasibility of classification. An XGBoost classifier is then evaluated on 1D and 2D conductance data, including noise robustness.\"}]","Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements | PDF",1785726819,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-detection-of-majorana-zero-modes-from-zero-bias-peak-measurements","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-detection-of-majorana-zero-modes-from-zero-bias-peak-measurements/119882/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What experimental signal is used to identify Majorana zero modes in this work?","Question",{"text":75,"@type":76},"The method focuses on zero-bias peaks in tunneling differential conductance, which are measured in scanning tunneling spectroscopy and serve as the central input data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is identifying MZMs from zero-bias peaks challenging?",{"text":80,"@type":76},"Topologically trivial states can also produce spurious zero-bias peak signals, making zero-bias peaks an ambiguous indicator without additional discrimination.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach train and validate the classifier?",{"text":84,"@type":76},"Training data are generated via quantum transport simulations from tight-binding models, and persistent cohomology analysis is used to confirm the feasibility of classification. An XGBoost classifier is then evaluated on 1D and 2D conductance data, including noise robustness.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]