An Efficient Continuous-Variable Quantum Key Distribution with Parameter Optimization Using Elitist Elk Herd Random Immigrants Optimizer and Adaptive Depthwise Separable Convolutional Neural Network

Vidhya Prakash Rajendran (), Deepalakshmi Perumalsamy (), Chinnasamy Ponnusamy and Ezhil Kalaimannan
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Vidhya Prakash Rajendran: College of Engineering and Technology, University of Technology and Applied Sciences, Nizwa 611, Oman
Deepalakshmi Perumalsamy: School of Computing, Kalasalingam Academy of Research and Education (KARE), Krishnankovil 626126, India
Chinnasamy Ponnusamy: School of Computing, Kalasalingam Academy of Research and Education (KARE), Krishnankovil 626126, India
Ezhil Kalaimannan: Department of Cyber Security and Information Technology, University of West Florida, Ferry Pass, FL 32514, USA

Future Internet, 2025, vol. 17, issue 7, 1-23

Abstract: Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate.

Keywords: quantum memory; continuous variable-quantum key distribution; CHMAC-EPQH; 2E-HRIO; technology; system; process (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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