Secure and efficient computing of AI is highly essential in modern defense and cloud infrastructure, for which traditional electronic architecture often faces performance and security bottlenecks. This paper presents an integrated framework that incorporates QKD with photonic neural networks to realize quantum-secured ultrafast AI computation. The integrated framework establishes QKD-secured channels for securing AI data transmission while using optical neural networks for high-speed inference and low-energy processing. Experimental simulation confirms the integrated approach attains as high as 40% latency reduction and 35% energy saving when compared with state-of-the-art conventional AI systems secured by classical cryptography. Moreover, the integrated QKD enables provably secure key exchange resistant against both classical and quantum attacks. This work thus points out the potential of quantum-photonic integration as a base for scalable, secure, and high-efficiency AI computing both in defense and cloud applications.
Secure and efficient computing of AI is highly essential in modern defense and cloud infrastructure, for which traditional electronic architecture often faces performance and security bottlenecks. This paper presents an integrated framework that incorporates QKD with photonic neural networks to realize quantum-secured ultrafast AI computation. The integrated framework establishes QKD-secured channels for securing AI data transmission while using optical neural networks for high-speed inference and low-energy processing. Experimental simulation confirms the integrated approach attains as high as 40% latency reduction and 35% energy saving when compared with state-of-the-art conventional AI systems secured by classical cryptography. Moreover, the integrated QKD enables provably secure key exchange resistant against both classical and quantum attacks. This work thus points out the potential of quantum-photonic integration as a base for scalable, secure, and high-efficiency AI computing both in defense and cloud applications.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.
The exponential growth of the Internet of Things (IoT) and its integration into critical infrastructures has elevated the need for robust and future-proof security mechanisms. Traditional cryptographic systems, while effective today, are increasingly vulnerable to advancements in quantum computing. This paper explores the integration of Quantum Key Distribution (QKD) as a novel security paradigm for next-generation IoT ecosystems. QKD leverages the principles of quantum mechanics to enable unconditionally secure key exchange, immune to computational attacks. We propose an architecture where QKD-enabled edge gateways and IoT nodes facilitate real-time secure communication through quantum-secured channels. The study analyzes the feasibility of implementing QKD in resource-constrained IoT environments and evaluates its performance in terms of key generation rate, latency, and energy consumption. Simulation results demonstrate that the proposed QKD-IoT framework significantly outperforms conventional encryption protocols in resisting both classical and quantum-based attacks, making it a viable foundation for building post-quantum resilient IoT infrastructures. The research also outlines the challenges related to scalability, hardware integration, and key management, and proposes solutions through hybrid QKD-classical cryptography models. This work paves the way for a quantum-secure IoT future, ensuring trust, privacy, and integrity in hyper-connected digital ecosystems.
This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reducing latency and energy consumption. The findings confirmed the primary hypothesis that AI-driven cryptographic optimization enhances both computational efficiency and system resilience against classical and quantum attack simulations. Lattice-based and code-based cryptosystems showed the most substantial performance gains when combined with AI learning models. Overall, the results validated that intelligent, adaptive encryption frameworks achieve measurable, statistically significant advantages in performance, scalability, and security across both cloud and IoT domains. These findings provide empirical evidence supporting the integration of AI-based decision systems into post-quantum cryptography for secure and sustainable digital infrastructures.
Fears regarding the security of any data are increasing drastically with the rapid deployment of cloud-based systems based on artificial intelligence (AI) systems, as of now, owing to the existence of quantum computing, which is threatening the existing cryptography schemes. To ensure secure communication and smart threat detection in cloud networks, this paper proposes a hybrid cloud-based security framework, Quantum Shield, which will deploy Quantum Key Distribution (QKD) and Quantum Machine Learning (QML). The QKD module relies on the BB84 protocol when generating encryption keys, sharing the Quantum Bit Error Rate (QBER) to detect eavesdropping and is considered to be safe. At the same time, the QML layer manipulates TensorFlow Quantum-executed Variational Quantum Circuits (VQC) to perform the processes of classification on encrypted data. QKD and QML modules are also scalably deployed on cloud infrastructure and the system is evaluated and measured within a simulated environment. The experiments suggest the framework is effective in identifying interception attacks and containing QBER below the security threshold in case of normal conditions. It also classifies better than its traditional counterparts. Further studies will be focused on quantum operation and the validation on a large scale. The proposed approach demonstrates a quantum and scalable approach to protecting cloud AI systems.
The widespread cryptographic methods RSA and AES have become more susceptible to quantum-based attacks because researchers developed quantum algorithms such as Shor's and Grover's. QKD chromosomes from quantum mechanics grant authorized parties the ability to exchange cryptographic keys securely alongside built-in eavesdropping detection functionality. In the proposed method a Quantum Key Distribution (QKD) is used to integrate into cloud environments to secure data against quantum threats which have become crucial security concerns today. A new security framework for combining QKD with standard cloud utilities strengthens both access privacy and verify data integrity and ensure authentication of user information. The system framework consists of key generation from quantum elements followed by distribution through quantum channels to support secure communication protocols for Software as a Service (SaaS) and Platform as a Service (PaaS) as well as Infrastructure as a Service (IaaS). The key performance evaluation of the QKD system includes measurements of key generation rate together with Quantum Bit Error Rate (QBER) and eavesdropping detection accuracy rates. The analysis against traditional cryptographic systems proves that QKD stands superior in quantum attack resilience. The proposed system demonstrates its efficiency and robustness through simulation outcomes as well as visual representations. The presented work demonstrates both the revolutionary capability of quantum technology for cloud security along with a deployable system architecture for future systems implementation. The research work advances the creation of cloud infrastructure systems which combine high security resilience to protect against future cyber threats.
The integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. This paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. The architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. Simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). The proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. This work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks.