Quantum computing is a threat to the existence of classical cryptographic authentication. Although quantum key distribution (QKD) has quantitative security, its real-life implementation is interfered with due to two reasons: the resource scalability and the availability of an authenticated classical channel. The presented paper proposes a new system, termed as Adaptive Hybrid Authentication Framework (AHAF), as a solution to these issues as it switches dynamically between classical, post-quantum, and quantum authentication protocols. An AHAF is based on a Reinforcement Learning (RL) based decision engine that transforms the problem of protocol selection into a Markov Decision Process (MDP). A multi-objective reward function is maximised by the RL agent, where the security posture, resources consumption in the system and performance are balanced according to real-time threats intelligence and resource availability. The AHAF was structured and verified in some high-fidelity simulation environment that incorporates NetSquid and NS-3. The findings clarify that the AHAF system is a capable solution that prevents a variety of simulated threats and meets the hypothesis of expected uptime (99.9 percent). This is achieved by carefully assigning expensive quantum protocols on a need-to-use basis and so providing QKD level of protection without incurring prohibitive performance cost of a fixed implementation. We have determined that an RL-based learning strategy most readily delivers workable and competitive road map towards feasible quantum-safe communication networks.
Quantum computing is a threat to the existence of classical cryptographic authentication. Although quantum key distribution (QKD) has quantitative security, its real-life implementation is interfered with due to two reasons: the resource scalability and the availability of an authenticated classical channel. The presented paper proposes a new system, termed as Adaptive Hybrid Authentication Framework (AHAF), as a solution to these issues as it switches dynamically between classical, post-quantum, and quantum authentication protocols. An AHAF is based on a Reinforcement Learning (RL) based decision engine that transforms the problem of protocol selection into a Markov Decision Process (MDP). A multi-objective reward function is maximised by the RL agent, where the security posture, resources consumption in the system and performance are balanced according to real-time threats intelligence and resource availability. The AHAF was structured and verified in some high-fidelity simulation environment that incorporates NetSquid and NS-3. The findings clarify that the AHAF system is a capable solution that prevents a variety of simulated threats and meets the hypothesis of expected uptime (99.9 percent). This is achieved by carefully assigning expensive quantum protocols on a need-to-use basis and so providing QKD level of protection without incurring prohibitive performance cost of a fixed implementation. We have determined that an RL-based learning strategy most readily delivers workable and competitive road map towards feasible quantum-safe communication networks.
The emergence of quantum computing challenges traditional security mechanisms, particularly in heterogeneous and resource-constrained IoT and smart environments that must satisfy DN25 requirements. This work introduces a reinforcement learning-driven model for the adaptive selection and orchestration of cryptographic algorithms. Acting as an intelligent decision layer, the system observes contextual, network, and operational metrics to recommend or enforce configurations combining classical schemes, post-quantum cryptography, and Quantum Key Distribution when available. The selection problem is formulated as a Markov Decision Process with state and action spaces aligned with protocol control flows and is embedded into a security middleware with negotiation and fallback mechanisms to ensure interoperability and policy compliance without modifying application logic. Experimental results demonstrate that the model dynamically adjusts key lengths, algorithm families, and security policies according to risk and resource conditions, increasing post-quantum cryptography and Quantum Key Distribution usage by up to 33.4% and 23.9% in high-risk scenarios while favoring low-latency classical or hybrid options for less critical traffic. The system achieves success rates above 78% while maintaining stable latency and resource usage during extended operation.
This research work describes a framework for secured and effective way of group interaction incorporating classical cryptography and quantum communication technique. This framework employs a classical cryptographic methods for group logistics like as transmitting of messages and membership management while employing quantum communication strategy for secure key distribution and related authentication. In order to increase the flexibility and data-security of the proposed system further, a supervisor learning based mechanism is incorporated into the framework where reinforcement learning will employed in controlling the interaction of the protocols and the decision-making processes in an active manner. This combination of classical, quantum and supervisor learning strategies gives a solution to the issues of scalability, efficiency, effective and timely actionable response to increasing cyber threats especially in the era of quantum computing. The framework is highly effective and secured for group interaction in distributed network infrastructures. It will be a leading the approach for advanced cryptographic mechanism in the future.
The rapid proliferation of Internet of Things (IoT) devices presents critical cybersecurity challenges, as traditional cryptographic methods often fail to provide both robust security and operational efficiency in resource-constrained environments. This paper introduces Adaptive Quantum Cryptography (AQC), a novel framework designed to secure next-generation IoT networks against evolving classical and quantum-computing threats. The AQC framework synergistically integrates Quantum Key Distribution (QKD) for unbreakable key exchange, Quantum Random Number Generators (QRNGs) for high-entropy key generation, and Post-Quantum Cryptography (PQC) for quantum-resistant data encryption. A key feature of the system is its dynamic adaptation mechanism, which leverages artificial intelligence to intelligently select and adjust cryptographic protocols in real-time based on current network conditions, device computational capabilities, and identified threat levels. This ensures optimal security without imposing excessive overhead on IoT devices. Our comprehensive experimental evaluation demonstrates that the AQC framework significantly outperforms classical techniques, achieving a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum-based attacks. By providing forward secrecy and robust authentication, the proposed AQC framework offers a scalable, energy-efficient, and future-proof security solution, paving the way for resilient and trustworthy IoT ecosystems in the post-quantum era.
Quantum Key Distribution (QKD) is a pioneering development in secure communication, an information-theoretic secure communications protocol that is founded on quantum mechanics. However, the effectiveness of such systems which safeguard the classical communication mediums against interception and impersonation depends on the strength of the authentication mechanisms that are used in the QKD systems. The proposed study is Hybrid Quantum Authentication Framework that is based on a combination of Wegman-Carter authentication, Quantum Digital Signature (QDS), and Post-quantum Cryptography (PQC) to counter new threats of Man-in-the-Middle (MITM), replay, and key exhaustion attacks. Based on the Quantum Encryption Dataset retrieved on the Kaggle platform, the framework was experimentally tested following the dataset augmentation to incorporate the authentication parameters and simulated attack labels. The results of the experiment prove that the hybrid model proposed can attain an authentication accuracy of 98.6%, which is significantly higher than that of standalone methods, MISM and replay attack detection rates of 99 and 98, respectively. The architecture lowers the key consumption by more than 40 percent, keeps the latency at 1.28 ms and the throughput at 92 percent, proving the scalability and computational efficiency of the architecture. Comparable performance and scalability studies also indicate that the model remains highly reliable as the size of the network increases with little growth in the latency. Generally, the suggested hybrid solution is robust, flexible and effective authentication of QKD networks and a viable solution in the next-generation quantum-safe communications infrastructure that can withstand classical and quantum attackers.
Combining quantum computing techniques in the form of amplitude amplification with classical reinforcement learning has led to the so-called “hybrid agent for quantum-accessible reinforcement learning”, which achieves a quadratic speedup in sample complexity for certain learning problems. So far, this hybrid agent has only been applied to stationary learning problems, that is, learning problems without any time dependency within components of the Markov decision process. In this work, we investigate the applicability of the hybrid agent to dynamic RL environments and thus to more realistic scenarios. To this end, we enhance the hybrid agent by introducing a dissipation mechanism and, with the resulting learning agent, perform an empirical comparison with a classical RL agent in an RL environment with a time-dependent reward function. Our findings suggest that the modified hybrid agent can adapt its behavior to changes in the environment quickly, leading to a higher average success probability compared to its classical counterpart.
Abstract— The advancement of quantum computing presents significant risks to conventional cryptographic systems that secure modern communication networks. This paper proposes an Adaptive Quantum–Classical Trust Communication System (AQCTCS), a hybrid secure messaging framework designed to enhance resilience against both classical and quantum threats. The system integrates three security layers: AES-256 for classical encryption, Post-Quantum Cryptography (PQC) for quantum-resistant protection, and a BB84-based Quantum Key Distribution (QKD) simulation for high-risk scenarios. A Trust Engine continuously evaluates security indicators, including anomaly detection parameters and Quantum Bit Error Rate (QBER), to compute a dynamic trust score. Based on this score, an Adaptive Security Controller automatically selects the appropriate encryption mode, balancing performance and security. The platform is implemented as a web application using React for the frontend, FastAPI for backend services, and Qiskit for QKD simulation. AQCTCS demonstrates an adaptive, scalable, and future-ready communication architecture suitable for post-quantum environments. Keywords— Quantum Key Distribution, Post-Quantum Cryptography, AES-256, Trust Engine, BB84 Protocol, Adaptive Encryption, Secure Messaging, QBER.