Blucher Engineering Proceedings

Dezembro 2024, vol.11, num.2 - X Simpósio Internacional de Inovação e Tecnologia
Full Article - Open Access Idioma principal | Segundo principal

Analysis of Machine Learning Techniques for Information Reconciliation in CV-QKD Systems: Challenges in Practical Scenarios

Analysis of Machine Learning Techniques for Information Reconciliation in CV-QKD Systems: Challenges in Practical Scenarios

Nooblath Neto, Mauro Queiroz; Ferreira Neto, Nelson Alves

Full Article:

"This paper reviews the challenges associated with implementing InformationReconciliation (IR) techniques in Continuous Variable Quantum Key Distribution (CVQKD) systems, focusing on integrating Machine Learning techniques and embeddedtechnologies such as Field-Programmable Gate Arrays (FPGAs). While CV-QKDsystems are compatible with existing telecommunications infrastructure and capableof high key generation rates, they face significant computational challenges in longdistance communication due to large decoding matrices. This study explores machinelearning approaches to reduce computational complexity and improve post-keyprocessing times, aiming to identify challenges and gaps that can guide future researchin enhancing the efficiency of CV-QKD systems."

"This paper reviews the challenges associated with implementing InformationReconciliation (IR) techniques in Continuous Variable Quantum Key Distribution (CVQKD) systems, focusing on integrating Machine Learning techniques and embeddedtechnologies such as Field-Programmable Gate Arrays (FPGAs). While CV-QKDsystems are compatible with existing telecommunications infrastructure and capableof high key generation rates, they face significant computational challenges in longdistance communication due to large decoding matrices. This study explores machinelearning approaches to reduce computational complexity and improve post-keyprocessing times, aiming to identify challenges and gaps that can guide future researchin enhancing the efficiency of CV-QKD systems."

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DOI: 10.5151/siintec2024-393504

Referências bibliográficas
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Como citar:

Nooblath Neto, Mauro Queiroz; Ferreira Neto, Nelson Alves; "Analysis of Machine Learning Techniques for Information Reconciliation in CV-QKD Systems: Challenges in Practical Scenarios", p-1113-1120. In: . São Paulo: Blucher, 2024.
ISSN 23577592, DOI 10.5151/siintec2024-393504

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