## 1 Introduction  
The volume and diversity of data have been growing rapidly across domains such as AI, healthcare, finance, and scientific simulation [1, 2, 3, 4, 5]. Traditional data are typically structured and well defined, such as relational tables and transaction logs. In contrast, modern data are dynamic, unstructured, and continuously generated from diverse sources,including real-time sensor streams, financial transactions, and user interactions. These data streams contain sensitive information, including patient vitals, account credentials, and behavioral patterns [6; 7; 8; 9]. Additionally, unstructured data continue to grow rapidly due to real-time generation and continuous accumulation in data-intensive applications [10; 11]. This continued growth in volume and sensitivity has made the ability to securely process such large-scale files a critical system requirement. As data become increasingly large-scale, complex, and security-critical, computing infrastructures have adopted distributed and heterogeneous architectures. Cloud systems such as AWS, Google Cloud, and Azure [12; 13; 14] support scalability and high availability, while also providing robust security environments. These platforms implement protocols like TLS [15] for secure transmission and Identity and Access Management (IAM) [16] for account control. The emergence of quantum computing, however, introduces new security threats, as these infrastructures typically rely on classical encryption methods to protect sensitive data [17; 18; 19]. Quantum computers offer high computational efficiency but undermine core assumptions of existing classical cryptographic algorithms. For example, [PERSON]'s algorithm [20] breaks RSA and ECC by solving prime factorization and discrete logarithms, while [PERSON]'s algorithm [21] accelerates brute-force key searches against symmetric schemes like AES [22]. Although AES remains relatively secure under quantum attacks with sufficient key length (e.g., AES-256), its security depends on the assumption that the symmetric key is established securely. However, since RSA or ECC is commonly used to exchange AES session keys, the entire encryption becomes vulnerable if the key-exchange mechanism is broken by quantum adversaries. To address this challenge, encryption schemes should be designed to provide security and compatibility in both classical and quantum computing environments. Among available solutions, Post-Quantum Cryptography (PQC) [23] and Quantum Key Distribution (QKD) [24] are the most widely utilized techniques for quantum-resilient security. PQC relies on hard mathematical problems to resist quantum attacks [25; 26; 27], while QKD uses quantum mechanics to distribute keys securely [28; 29; 30; 31]. However, both PQC and QKD face limitations when used independently. PQC can securely encapsulate AES keys for data encryption, but lacks mechanisms to verify the integrity of the encapsulated key or prevent tampering. In distributed systems where the same key is broadcast to multiple nodes, PQC without additional authentication cannot ensure the origin or integrity of the transmitted key, making it vulnerable to man-in-the-middle (MITM) attacks and ciphertext forgery [32; 33; 34]. In contrast, QKD enables secure key exchange using quantum channels but offers no way to verify the identity of the sender. Without authentication support, a receiver cannot confirm the origin of the key, leaving the system open to impersonation or interception. QKD is fundamentally designed for key distribution and lacks mechanisms to store, format, or directly apply keys in encryption workflows [28; 35; 36]. For practical data protection, it should be integrated with a symmetric encryption method. To overcome these limitations, recent studies have actively explored combining these techniques to strengthen cryptographic systems. Table 1 summarizes recent approaches integrating quantum and classical cryptography. Specifically, [PERSON] et al.