\cite{Mahaseth_2025}Quantum computing threatens the foundational hardness assumptions of classical public-key cryptography. We propose a unified framework that combines: (i) NISTstandardized post-quantum cryptography (PQC), (ii) quantum key distribution (QKD) for information-theoretic key exchange, and (iii) AI-based anomaly detection for operational resilience, orchestrated by a reinforcement learning (RL) controller that adaptively selects among PQC, QKD, and hybrid modes. We contribute five algorithms/pseudocode blocks-PQC-Migrate, QKD+AI-Guard, RL-Channel, a QAOA outline, and a Quantum Kernel Classifier-and present illustrative experiments: a BB84style key distribution histogram, anomaly decision regions, an RL reward convergence curve, an end-to-end architecture, migration phases, and a kernel baseline on nonlinear data. We discuss current challenges (NISQ noise, crypto-agility, QKD footprint, side-channels, benchmarking integrity, governance, and global competition) with concrete mitigations.
\cite{Mahaseth_2025}Quantum computing threatens the foundational hardness assumptions of classical public-key cryptography. We propose a unified framework that combines: (i) NISTstandardized post-quantum cryptography (PQC), (ii) quantum key distribution (QKD) for information-theoretic key exchange, and (iii) AI-based anomaly detection for operational resilience, orchestrated by a reinforcement learning (RL) controller that adaptively selects among PQC, QKD, and hybrid modes. We contribute five algorithms/pseudocode blocks-PQC-Migrate, QKD+AI-Guard, RL-Channel, a QAOA outline, and a Quantum Kernel Classifier-and present illustrative experiments: a BB84style key distribution histogram, anomaly decision regions, an RL reward convergence curve, an end-to-end architecture, migration phases, and a kernel baseline on nonlinear data. We discuss current challenges (NISQ noise, crypto-agility, QKD footprint, side-channels, benchmarking integrity, governance, and global competition) with concrete mitigations.
Quantum computing threatens the foundational hardness assumptions of classical public-key cryptography. We propose a unified framework that combines: (i) NISTstandardized post-quantum cryptography (PQC), (ii) quantum key distribution (QKD) for information-theoretic key exchange, and (iii) AI-based anomaly detection for operational resilience, orchestrated by a reinforcement learning (RL) controller that adaptively selects among PQC, QKD, and hybrid modes. We contribute five algorithms/pseudocode blocks-PQC-Migrate, QKD+AI-Guard, RL-Channel, a QAOA outline, and a Quantum Kernel Classifier-and present illustrative experiments: a BB84style key distribution histogram, anomaly decision regions, an RL reward convergence curve, an end-to-end architecture, migration phases, and a kernel baseline on nonlinear data. We discuss current challenges (NISQ noise, crypto-agility, QKD footprint, side-channels, benchmarking integrity, governance, and global competition) with concrete mitigations.
Quantum Key Distribution (QKD) provides key exchange security grounded in quantum physics, but practical deployments still face constraints in scalability, noise tolerance, real-time operations, and implementation security. In parallel, machine learning (ML) has proven effective for adaptive control, prediction, and anomaly detection in complex cyber-physical systems. This paper reviews how AI/ML can be integrated into QKD to improve robustness and operational security without weakening core cryptographic guarantees. We present an AI-supported QKD architecture, summarize key performance metrics (QBER, secret key rate, and throughput), and outline how learning-based control can tune parameters dynamically under varying channel conditions. We also discuss security-operations integration (SOC visibility, alerting, and key lifecycle telemetry) and provide a reinforcement learning (RL) case study to illustrate adaptive parameter selection. Finally, we analyze threats introduced by AI and identify practical design constraints (fail-safe behavior, policy bounds, and verification) required to preserve quantum-security principles while enabling quantum-safe integration with post-quantum cryptography workflows.
Quantum computing threatens classical cryptography, which has prompted the creation of post-quantum cryptography (PQC) and quantum key distribution (QKD). Hybrid cryptography integrates classical, post-quantum, and quantum techniques for achieving multi-layer security and smooth migration towards quantum-safe infrastructures. In this paper, practical deployment patterns, the employment of cryptographic agility, and integration of PQC with QKD are presented with emphasis on hybrid methods as a key strategy for quantum-secure communication.
This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion. Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns. A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience. The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.
The current security systems cannot be considered long term sustainable because there is a quantum rate of development of the computing which directly and immediately threatens the classical cryptograph systems. In this respect, the research paper will undertake efforts in giving the concept of an AI-led adaptive mechanism of Quantum Key Distribution (QKD) protocols that will be self-sufficient in guarding and overseeing quantum communication networks against countermeasures towards the constantly evolving threats. The model combines quantum resources management and real-time threat analysis and optimization of dynamic parameter into one flow. Quantum channel noises can be predictedively implemented on machine learning using reinforcement learning, continued intrusion and anomaly detection on neural networks, and independent optimization of the parameters of QKD in dynamical communication systems. Post-quantum cryptography PQC algorithm-based lattice, hash-based and multivariate cryptography are also provided in the system as a backup mechanism to provide the confidence of the hybrid communication environments with the future compatibility of the classical and quantum infrastructural systems. In other cases, the framework will enhance quantum- enhanced blockchain where the AI-enhanced verification enhances the integrity of the distributed registries and resilience in the interparty communication networks. The ethical considerations and the need to have light and interoperative standards are the other considerations since it needs to be able to deploy on a large scale. Instead of calculating the performance standards, the provided research involves the conceptual architecture diagram of the way the predictive AI models can be logically aligned with the natural security features of quantum mechanics to a new generation cryptographic ecosystem that will be able to fight the classical and the quantum adversaries simultaneously.
The rapid advancement of quantum computing threatens the foundations of classical cryptography, creating severe risks for resource-constrained IoT systems widely used in healthcare, autonomous vehicles, and industrial automation. Conventional algorithms like RSA and ECC are vulnerable to Shor's and Grover's algorithms, exposing IoT to passive and active quantum-based attacks. This manuscript proposes a quantum-safe framework integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Artificial Intelligence (AI) to ensure confidentiality, integrity, and resilience. The framework leverages lattice, hash, and code-based PQC, lightweight cryptographic protocols, hybrid security models, and efficient key management to protect IoT communications. By combining PQC, QKD, and AI-driven adaptive security, the framework offers scalable, sustainable, and future-proof protection in the post-quantum era.