## 1 Introduction  
One of the most important methods in quantum cryptography, Quantum Key Distribution (QKD), enables secure communication through the application of quantum mechanical principles, complemented by an authenticated classical communication channel [1]. According to [2], there are two main types of QKD: discrete variable QKD and continuous variable QKD. Discrete Variable Quantum Key Distribution (DV-QKD) uses different quantum states, such as photon polarization, to encode information [3]. In real-world contexts, DV-QKD encounters obstacles such photon loss, inefficient detectors, and limited communication distance [4]. Information in the continuous features of quantum states, including the amplitude and phase of light, is encoded using Continuous Variable QKD (CV-QKD) [5]. While CV-QKD offers advantages such as higher secret key rates at short distances and compatibility with standard telecom infrastructure, it is generally more sensitive to channel loss than DV-QKD. As a result, CV-QKD typically supports shorter transmission distances compared to DV-QKD. However, ongoing advancements in reconciliation efficiency and noise management continue to improve its practical range, making it a promising candidate for metropolitan-scale quantum communication systems [6; 7]. Additionally, long-distance quantum communication has been greatly enhanced by the invention of CV-QKD quantum memory [8]. Improvements in optimization, security, and error correction may result from the incorporation of ML and DL into QKD systems as quantum communication technology advances [9; 10]. According to [11], many ML algorithms, including Random Forest (RF) and Support Vector Machines (SVM), are frequently used to enhance the detection of quantum states. When dealing with the intricacies of quantum systems, these methods become invaluable in quantum mechanics [12; 13]. In addition, Convolutional Neural Networks (CNNs) and other deep learning models have been employed to forecast quantum systems' channel, noise, and loss, leading to enhanced efficiency and dependability [14]. The development of quantum memory for continuous variables, taking the decoherence rate into account, has not, however, been the focus of any extant work. This led to the research methodology of 2E-HRIO and ADS-CNN-based efficient continuous-variable quantum key distribution with optimized parameters. Continuous Variable Quantum Key Distribution (CV-QKD) systems signify a notable progression in secure quantum communication; yet, some essential issues seen in the current literature have been inadequately handled. Current research has predominantly neglected the advancement of efficient quantum memory for continuous variables that incorporate decoherence effects, which is crucial for the preservation of quantum coherence over time. Several recent studies have advanced the development of continuous-variable quantum key distribution (CV-QKD), yet critical challenges remain unaddressed. Reference [15] demonstrated the significant vulnerability of CV-QKD systems to channel noise and loss, which severely degrade performance and security. The study in [16] highlighted the ongoing nature of quadrature measurements and the complexity of the associated error correction processes, underscoring the computational overhead that limits practical deployment. However, these works did not consider the impact of quantum memory decoherence, which is critical for secure and long-term key storage. In addition, [17] also identified vulnerabilities at the detector level but did not explore authentication mechanisms for quantum devices, such as transmitters and receivers--leaving systems exposed to impersonation and device forgery attacks. The authors in [18] also acknowledged certainoperational weaknesses in CV-QKD protocols but did not sufficiently address the threat of side-channel attacks, resulting in residual security gaps.