Hybrid Quantum Key Distribution Framework: Integrating BB84, B92, E91, and GHZ Protocols for Enhanced Cryptographic Security

The rapid evolution of quantum computing poses a significant threat to classical cryptographic systems like Rivest‐Shamir‐Adleman (RSA) and Elliptic Curve Cryptography (ECC), which rely on the computational hardness of problems such as integer factorization and discrete logarithms. Quantum algorithms such as Shor's algorithm can solve these problems quickly, which undermines the foundations on which classical cryptography relies. Quantum Key Distribution (QKD) is an alternative to the classical methods, which promises information‐theoretic security based on quantum mechanics. Currently, there are existing QKD protocols, including BB84, B92, E91, and GHZ, all of which exhibit various real‐world limitations. For example, these QKD protocols can be vulnerable to a variety of side‐channel attacks (e.g., detector blinding, photon‐number‐splitting) and neglect to consider fluctuating network conditions. Current QKD protocols also fail to accommodate scalability for many‐to‐many or noise‐limited scenarios. Many implementations of the existing QKD protocols and other common forms of networks remain static, relying on arbitrary decisions of fixed values that yield simple linear conclusions that can be predicted and targeted in the real‐world environment. To address these omissions, we propose a new framework for dynamic or adaptive hybrid QKD in which we incorporate BB84, B92, E91, and GHZ into one common approach with all protocols selected based on a probability‐weighted distribution of (0.3, 0.2, 0.3, 0.2). In the hybrid QKD implementation, the probability weights of protocol selection are assigned with partiality toward BB84, E91, B92, and GHZ, respectively. This may also introduce a higher variety of protocols and diversity in approaches that will further limit cross‐protocol possibilities of attack vectors, while increasing the possible flexibility of adaptability in attacked situations. In addition, we incorporate an artificial intelligence (AI)‐based optimization module using a neural network to evaluate local environmental noise and quantum bit error rate (QBER) in real time. It adjusts protocol selection probabilities dynamically based on both historical and live operational data to optimize throughput while maintaining low error rates. The system architecture supports modular and parallel operation and has been mapped out and designed to be scalable and compatible with future quantum networks. We test our system using IBM's Qiskit AerSimulator utilizing a 14‐qubit register with 100 rounds of a 1% depolarization noise model, which significantly outperformed static hybrids such as Chen et al. in terms of both key rate and QBER. Our system consistently produced an average QBER of 0.02 and a key generation rate of 12 bits per round. E91 consistently produced CHSH violating confirming the fidelity of the entanglement, while BB84 displayed no QBER on all rounds. This work demonstrates the first fully integrated, AI‐assisted, dynamic hybrid QKD system. It includes all advantageous features of a QKD protocol: the dynamic adaptability of the protocol allows for a performance driven environment, the use of entangled states provides increased security, and a bottom‐up approach to real‐time optimization creating a robust, scalable, and agnostic system to any hardware used by post‐quantum cryptographic infrastructures.