[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119177-en":3,"doc-seo-119177-105":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},119177,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting the Onset of Quantum Synchronization Using Machine Learning","An environment-induced spontaneous synchronization can emerge between two qubits in an open-system setting. The study applies a machine learning approach to predict when synchronization occurs using only early-time expectation values of qubit observables, across three models spanning global and local dissipation regimes. A k-nearest neighbors algorithm estimates the long-time synchronization behavior from early dynamics. Results show high-precision identification of distinct synchronization phenomena and robustness to measurement errors, initialization imperfections, and environment-temperature deviations.","arXiv :2308 . 15330v2 [ quant-ph] 9 May 2024  \nPredicting the onset of quantum synchronization using machine learning  \nF. Mahlow, 1, ∗ B. Çakmak,2, 3 G. Karpat,4 İ . Yalçınkaya,5 and F. F. Fanchini 1, 6  \n1 Faculty of Sciences, UNESP - São Paulo State University, 17033-360 Bauru-SP, Brazil  \n2 Department of Physics, Farmingdale State College—SUNY, Farmingdale, NY 11735, USA  \n3 College of Engineering and Natural Sciences, Bahçeşehir University, Beşiktaş, Istanbul 34353, Turkiye  \n4 Department of Physics, Faculty of Arts and Sciences,  \nİzmir University of Economics, İzmir, 35330, Turkey  \n5 Department of Physics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Praha 1-Staré Město, Czech Republic  \n6 QuaTI - Quantum Technology & Information, 13560-161 São Carlos-SP, Brazil (Dated: May 13, 2024)  \nWe have applied a machine learning algorithm to predict the emergence of environment-induced spontaneous synchronization between two qubits in an open system setting. In particular, we have considered three different models, encompassing global and local dissipation regimes, to describe the open system dynamics of the qubits. We have utilized the k-nearest neighbors algorithm to estimate the long-time synchronization behavior of the qubits only using the early time expectation values of qubit observables in these three distinct models. Our findings clearly demonstrate the possibility of determining the occurrence of different synchronization phenomena with high precision even at the early stages of the dynamics using a machine learning-based approach. Moreover, we show the robustness of our approach against potential measurement errors in experiments by considering random errors in the qubit expectation values, initialization errors, as well as deviationsin the environment temperature. We believe that the presented results can prove to be useful in experimental studies on the determination of quantum synchronization.  \nI. INTRODUCTION  \nMachine learning is a rapidly growing field of research that involves the use of computational algorithms to estimate complex functions using large amounts of available data. These functions can then be used to make some predictions and identify patterns in given datasets. The main difference between the machine learning approach and other statistical models is that it allows a computer program to improve its performance or learn, without the need for explicit programming [1] . In recent years, machine learning methods have achieved remarkable success in a wide range of applications, including natural language processing, image recognition, drug discovery, and finance. Machine learning algorithms have been applied in many areas, including computer science, medicine, biology, and even social sciences [2] . In physics, machine learning techniques have been extensively used in various research fields, encompassing cosmology, particle physics, condensed matter physics, and quantum computing [3] . Synchronization is a widespread phenomenon that occurs across many different systems, from natural systems like the beating of a heart or flashing of fireflies to social systems such as the behavior of a crowd. Physical systems can exhibit synchronous behavior in two ways, namely, forced and spontaneous. When two or more systems are forced to oscillate in unison by an outside influence, such as the regulation of heart rate by an external pacemaker through electrical pulses, this is  \n∗ [f.mahlow@unesp.br](f.mahlow@unesp.br)  \nknown as forced synchronization. On the contrary, spontaneous synchronization takes place when two or more systems naturally synchronize in the absence of any external agent. Synchronization in classical systems has been studied in a variety of contexts over the past few decades with many interesting outcomes [4–6] . Consequently, the study of this universal phenomenon has been extended to the quantum domain.  \nNevertheless, it nee","cbCaibGBjyhU4CUg","https://ap.wps.com/l/cbCaibGBjyhU4CUg","pdf",1638885,1,13,"English","en",105,"# Introduction\n## Machine learning in physics\n## Synchronization: forced vs spontaneous\n## Environment-induced spontaneous synchronization\n## Prior ML studies on synchronization\n## Scope and approach of this work","[{\"question\":\"What synchronization phenomenon does the document aim to predict?\",\"answer\":\"It predicts environment-induced spontaneous synchronization that emerges between two qubits’ dynamics in an open system.\"},{\"question\":\"How does the machine learning method use the available data?\",\"answer\":\"A k-nearest neighbors algorithm estimates long-time synchronization behavior using only early-time expectation values of qubit observables.\"},{\"question\":\"Is the approach robust to experimental imperfections?\",\"answer\":\"Yes. The study tests robustness by considering random measurement errors, initialization errors, and deviations in environment temperature.\"}]","Predicting the Onset of Quantum Synchronization Using Machine Learning | PDF",1785722932,33,{"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},"predicting-the-onset-of-quantum-synchronization-using-machine-learning","",{"@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/predicting-the-onset-of-quantum-synchronization-using-machine-learning/119177/",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 synchronization phenomenon does the document aim to predict?","Question",{"text":75,"@type":76},"It predicts environment-induced spontaneous synchronization that emerges between two qubits’ dynamics in an open system.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning method use the available data?",{"text":80,"@type":76},"A k-nearest neighbors algorithm estimates long-time synchronization behavior using only early-time expectation values of qubit observables.",{"name":82,"@type":73,"acceptedAnswer":83},"Is the approach robust to experimental imperfections?",{"text":84,"@type":76},"Yes. The study tests robustness by considering random measurement errors, initialization errors, and deviations in environment temperature.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]