[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121580-en":3,"doc-seo-121580-105":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},121580,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","A Framework for Improving the Performance of QKDN using Machine Learning Approach","A secure cryptographic key exchange between distant parties is achieved through Quantum Key Distribution Network (QKDN), leveraging principles of quantum physics for confidentiality and eavesdropping detection. Although QKDN architectures exist, improving performance using machine learning and soft computing remains limited. The framework describes the role of each QKDN layer, identifies constraints across layers, and proposes a machine-learning-based approach to enhance overall QKDN performance for commercialization and reliable, end-to-end operation.","A Framework for Improving the Performance of QKDN using Machine Learning Approach  \nArthi Ra , Saravanan Ab , Nayana J Sc , and Chandresh MuthuKumarand  \na,c,d Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India.  \nb Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai, India [arthir2@srmist.edu.in](arthir2@srmist.edu.in), [dean.academic@srmrmp.edu.in](dean.academic@srmrmp.edu.in), [nj1672@srmist.edu.in](nj1672@srmist.edu.in), [cm7061@srmist.edu.in](cm7061@srmist.edu.in)  \nKEYWORD Abstract  \nQubits; Quantum Layer; Machine Learning; Quantum Key Distribution Network (QKDN); Cryptography  \nA dependable secure correspondence hosts been given between two far off gatherings by Key sharing and Quantum Key Distribution (QKD) . This was concentrated widely, as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques deal with Quantum Key Distribution Network (QKDN), however only few of them using Machine Learning (ML) and soft computing techniques are available in improving QKDN. ML can analyze data and improve itself based on model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of machine learning technologies. Because of the benefits that ML provides, it can facilitate in improving and resolving issues in QKDNfor the commercialization of QKDN. The proposed work provides a detailed understanding of role each layer of QKDN, addressing the limitations of each layer, and suggesting a framework for improving the performance of QKDNby applying Machine Learning technique.  \n1. Introduction  \nCommunication technology is an essential part of our daily lives, present communication technologies depend on the high-speed optical fiber communication network for its infrastructure. (Spurny et al., 2022) has authored however, these optical fiber networks are vulnerable to eavesdroppers because they are susceptible to line and route interruptions, thereby resulting in data leakages. This is a serious threat to the critical information of the public, is impair of Government confidentiality and can jeopardize business. This problem can be tackled by using the cipher communication, this can protect the confidential information against the eavesdroppers by using either symmetric or asymmetric cryptography. In order to provide security, the key has to be designed in such a way that it cannot be decoded, so the computational complexity is what guarantees security. Yet for the cipher/cryptographic transmission of highly confidential data, we must have an extremely secure method of  \nAdvances in Distributed Computing and Artificial Intelligence Journal  \n©Ediciones Universidad de Salamanca / cc by-nc-nd  \n1  \nADCAIJ, Regular Issue 1 [http://adcaij.usal.es](http://adcaij.usal.es)  \ncrypto-key sharing between the remote parties.  \nFigure 1: Architecture of QKDN  \nThe principles of quantum physics protect the data in Quantum Key Distribution (QKD), making it a perfect solution for sharing crypto-keys securely between distant par-ties. In QKD, qubits (quantum information carriers/units of quantum information) are used to encode information that is sent from point to point on a quantum channel. Information can be theoretically secured with QKD technology by using one-time-pad encryption and detecting potential eavesdropping can also be possible as reported in (ITU-TY.supp, 2021) . A Quantum Key Distribution Network (QKDN) is a technology that allows QKD to achieve greater reach and availability as authored by (Choi et al., 2021) . Keys between QKDN nodes can be exchanged through QKDN links, but when they are not connected, they get exchanged through key relays. QKDN, not withstanding, is recognized from  \nAdvances in Distributed Computing and Artificial Intelligence Journal  \n©Ediciones Universidad de Salamanca / cc by-nc-nd  \n2  \nADCA","cbCaigFHKruGxvEn","https://ap.wps.com/l/cbCaigFHKruGxvEn","pdf",710944,1,14,"English","en",105,"# Introduction\n## Quantum Key Distribution and QKDN basics\n## Layered architecture of QKDN\n## Motivation for machine learning in QKDN","[{\"question\":\"What problem does the proposed framework address in QKDN?\",\"answer\":\"It targets limited approaches for improving QKDN performance using machine learning and soft computing techniques, aiming for better operation for secure key exchange and commercialization.\"},{\"question\":\"How does QKDN relate to QKD and qubits?\",\"answer\":\"QKDN extends QKD to provide wider reach and availability, using qubits as quantum information carriers to encode data on quantum channels with protections from quantum physics.\"},{\"question\":\"What layers are identified in the QKDN architecture?\",\"answer\":\"The architecture includes a Quantum Layer, Key Management Layer, QKDN control layer, User Network Management Layer, Quantum Management Layer, and Service layer, each with distinct functions.\"}]","A Framework for Improving the Performance of QKDN using Machine Learning Approach | 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problem does the proposed framework address in QKDN?","Question",{"text":75,"@type":76},"It targets limited approaches for improving QKDN performance using machine learning and soft computing techniques, aiming for better operation for secure key exchange and commercialization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does QKDN relate to QKD and qubits?",{"text":80,"@type":76},"QKDN extends QKD to provide wider reach and availability, using qubits as quantum information carriers to encode data on quantum channels with protections from quantum physics.",{"name":82,"@type":73,"acceptedAnswer":83},"What layers are identified in the QKDN architecture?",{"text":84,"@type":76},"The architecture includes a Quantum Layer, Key Management Layer, QKDN control layer, User Network Management Layer, Quantum Management Layer, and Service layer, each with distinct 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