[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123386-en":3,"doc-seo-123386-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123386,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Noise classification in three-level quantum networks by Machine Learning","We investigate a machine learning based classification of noise acting on a small quantum network to detect spatial or multilevel correlations and to analyze their relationship with Markovianity. A three-level system is controlled via coherent population transfer, using different pulse amplitude combinations as inputs to train a feedforward neural network. Supervised learning distinguishes classical dephasing noise types with over 99% accuracy for non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian cases. Correlations in Markovian noise cannot be discriminated. The method remains robust under statistical measurement errors and works with limited sample sizes, supporting practical experimental implementation and enabling future classification of spatial correlations in quantum architectures.","PAPER • OPEN ACCESS  \nNoise classification in three-level quantum networks by Machine Learning  \nTo cite this article: Shreyasi Mukherjee et al 2024 Mach. Learn. : Sci. Technol. 5 045049  \nView the article online for updates and enhancements.  \nYou may also like  \n-Physics-inspired machine learning detects‘unknown unknowns’ in networks: discovering network boundaries from observable dynamics  \nMoshir Harsh, Leonhard Götz Vulpius and Peter Sollich  \n-An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks  \nMingtao Xia, Xiangting Li, Qijing Shen et al.  \n-Refinable modeling for unbinned SMEFT analyses  \nRobert Schöfbeck  \nThis content was downloaded from IP address [147.163.7.33](147.163.7.33) on 04/06/2025 at 11:02  \n Mach. Learn.: Sci. Technol. 5 (2024) 045049 [https://doi.org/10.1088/2632-2153/ad9193](https://doi.org/10.1088/2632-2153/ad9193)  \nOPEN ACCESS  \nRECEIVED  \n9 May 2024  \nREVISED  \n17 October 2024  \nACCEPTED FOR PUBLICATION 12 November 2024  \nPUBLISHED  \n26 November 2024  \nOriginal Content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPAPER  \nNoise classification in three-level quantum networks by Machine Learning  \nShreyasi Mukherjee1, Dario Penna2, Fabio Cirinn2, Mauro Paternostro3,4􀁂, Elisabetta Paladino1,5,6􀁂 , Giuseppe Falci1,5,6􀁂 and Luigi Giannelli1,5, ∗􀁂  \n1 2  \n3 4  \n5 6  \n∗  \nDipartimento di Fisica e Astronomia ‘Ettore Majorana’, Universit di Catania, Via S. Sofia 64, 95123 Catania, Italy  \nLeonardo S.p.A., Cyber & Security Solutions, 95121 Catania, Italy  \nUniversit degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio Segrè,via Archirafi 36, I-90123 Palermo, Italy Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queens University, Belfast BT7 1NN, United Kingdom  \nINFN, Sezione di Catania, 95123 Catania, Italy  \nCNR-IMM, UoS Universit, 95123 Catania, Italy  \nAuthor to whom any correspondence should be addressed.  \nE-mail: [luigi.giannelli@dfa.unict.it](luigi.giannelli@dfa.unict.it)  \nKeywords: machine learning for quantum, three-level system, noise classification,(non-)Markovianity, noise correlations, quantum network  \nAbstract  \nWe investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three  \nnon-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than 99% accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures.  \n1. Introduction  \nNowadays, quantum systems can be controlled with impressive accuracy [1] to perform new tasks by exploiting their coherence properties, such as superposition and entanglement [2] . However, quantum behavior fades away because of environmental noise that leads to a loss of accuracy of quantum operations and decoherence [3] . The development of strategies to counteract these effects is therefore of crucial importance for progress in quantum technology. In this context, machine learning (ML) is proving to be an innovative and powerful diagnostic tool [4–","cbCaiitHBEEKE39H","https://ap.wps.com/l/cbCaiitHBEEKE39H","pdf",896761,1,15,"English","en",105,"# Introduction\n## Motivation: noise and decoherence in quantum technologies\n## Role of machine learning as a diagnostic tool\n## Strategies for decoherence mitigation and environment characterization\n## Challenge of characterizing multilevel nodes and multi-qubit architectures","[{\"question\":\"What problem does the document address in quantum networks?\",\"answer\":\"It addresses classifying noise acting on a small three-level quantum network, with a focus on detecting spatial or multilevel correlations and understanding the interplay with Markovianity.\"},{\"question\":\"How is the machine learning model trained?\",\"answer\":\"The three-level system is driven by coherent population transfer, using different pulse amplitude combinations as inputs to train a feedforward neural network.\"},{\"question\":\"What types of noise can be classified, and what accuracy is achieved?\",\"answer\":\"The method classifies distinct types of classical dephasing noise, achieving more than 99% accuracy for non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises.\"},{\"question\":\"Can the method discriminate correlations of Markovian noise?\",\"answer\":\"No. Correlations of Markovian noise cannot be discriminated with the proposed method.\"}]","Noise classification in three-level quantum networks by Machine Learning | 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problem does the document address in quantum networks?","Question",{"text":75,"@type":76},"It addresses classifying noise acting on a small three-level quantum network, with a focus on detecting spatial or multilevel correlations and understanding the interplay with Markovianity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model trained?",{"text":80,"@type":76},"The three-level system is driven by coherent population transfer, using different pulse amplitude combinations as inputs to train a feedforward neural network.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of noise can be classified, and what accuracy is achieved?",{"text":84,"@type":76},"The method classifies distinct types of classical dephasing noise, achieving more than 99% accuracy for non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises.",{"name":86,"@type":73,"acceptedAnswer":87},"Can the method discriminate correlations of Markovian noise?",{"text":88,"@type":76},"No. Correlations of Markovian noise cannot be discriminated with the proposed method.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & 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