Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.In this work, we propose a vision of distributed architecture and cyber-security framework that uniquely synergizes secure computation, Physical Quantum Key Distribution (PQKD), NIST Post- Quantum Cryptography (PQC) efforts, and AI/ML algorithms to achieve breach-resilient, functional and efficient cyber-security services. At the heart of our proposal lies a new Multi-Party Computation Quantum Network Core (MPC-QNC) that enables fast and yet quantum-safe execution of distributed computation protocols via integration of PQKD infrastructure and hardware- acceleration elements. We showcase the capabilities of MPC- QNC by instantiating it for Public Key Infrastructures (PKI) and federated ML in our HDQPKI and TPQ-ML, frameworks, respectively. HDQPKI (to the best of our knowledge) is the first hybrid and distributed post-quantum PKI that harnesses PQKD and NIST PQC standards to offer the highest level of quantum safety with a breach-resiliency against active adversaries. TPQ-ML presents a post-quantum secure and privacy-preserving federated ML infrastructure.
Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.In this work, we propose a vision of distributed architecture and cyber-security framework that uniquely synergizes secure computation, Physical Quantum Key Distribution (PQKD), NIST Post- Quantum Cryptography (PQC) efforts, and AI/ML algorithms to achieve breach-resilient, functional and efficient cyber-security services. At the heart of our proposal lies a new Multi-Party Computation Quantum Network Core (MPC-QNC) that enables fast and yet quantum-safe execution of distributed computation protocols via integration of PQKD infrastructure and hardware- acceleration elements. We showcase the capabilities of MPC- QNC by instantiating it for Public Key Infrastructures (PKI) and federated ML in our HDQPKI and TPQ-ML, frameworks, respectively. HDQPKI (to the best of our knowledge) is the first hybrid and distributed post-quantum PKI that harnesses PQKD and NIST PQC standards to offer the highest level of quantum safety with a breach-resiliency against active adversaries. TPQ-ML presents a post-quantum secure and privacy-preserving federated ML infrastructure.
The integration of artificial intelligence (AI) and machine learning (ML) into critical infrastructure has raised urgent concerns about data and model security, particularly in light of emerging quantum computing capabilities. Quantum algorithms threaten to render classical cryptographic methods obsolete, exposing AI/ML systems to potential breaches in confidentiality, integrity, and availability. This paper investigates the implications of quantum computing for securing AI/ML data both in transit and at rest and explores the development of quantum-safe networking protocols and cryptographic techniques. I examine post-quantum cryptographic (PQC) solutions including lattice-based, code-based, and hash-based algorithms, alongside the role of quantum key distribution (QKD) and AI-enhanced security orchestration. The study further addresses secure edge intelligence, federated AI systems, and emerging standards for 6G and beyond. My findings highlight both the necessity and complexity of transitioning to quantum-resilient infrastructure. Key challenges include computational overhead, legacy interoperability, and ethical concerns around AI-powered surveillance in quantum-secured environments. The paper concludes by emphasizing the need for proactive policy, investment in quantum-safe R&D, and cross-sector collaboration to safeguard AI/ML infrastructure in the post-quantum era.
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.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
Quantum computing’s arrival signals a paradigm shift in computational power that may transform several sectors, including drug discovery, artificial intelligence, materials research, and optimization [2]. However, this transformative potential also presents a significant and imminent threat to cybersecurity. At the heart of this technological shift is the ability of quantum computers to tackle the complex mathematical challenges on which modern public-key cryptography relies. Quantum algorithms, most notably Shor’s algorithm, can break problems such as integer factorization and discrete logarithms far more efficiently than classical approaches. As a result, cryptographic methods that are widely trusted today—including RSA and Elliptic Curve Cryptography (ECC)—could become insecure once large-scale quantum computing becomes a reality[1], [8].This poses a profound challenge to the confidentiality, integrity, and authenticity of digital communications, financial networks, essential infrastructure, and national defense systems [9]. To counter this emerging risk, Post-Quantum Cryptography (PQC) has become an important area of active research. The goal of PQC is to design cryptographic techniques that can withstand attacks from both conventional computers and future quantum systems, thereby preserving long-term data security in the face of advancing quantum technologies [3].A number of promising Post-Quantum Cryptography (PQC) approaches are currently being explored in depth. Among them, lattice-based cryptography has attracted significant interest, as it relies on the computational difficulty of problems like Learning With Errors (LWE) and the Shortest Vector Problem (SVP), offering both robust security assurances and practical efficiency [4], [5]. Hash-based signature schemes, such as XMSS and SPHINCS+, offer security rooted in well-established hashing techniques and stateless signature construction [7]. Additionally, code-based cryptography, including systems inspired by the McEliece cryptosystem, remains attractive for its long-standing resistance to cryptanalytic attacks [6]. Other avenues include multivariate quadratic systems and isogeny-based cryptography, each presenting unique benefits and trade-offs in terms of key size, performance, and security robustness [8].A central force driving global standardization is the NIST Post-Quantum Cryptography Standardization Project, initiated in 2016 to evaluate and establish new quantum-resilient cryptographic standards [3]. Several algorithms have already advanced to the final standardization phase, while others remain candidates for further evaluation.Despite this progress, transitioning to PQC introduces practical challenges. Integrating new cryptographic algorithms into current digital ecosystems demands considerable computational and infrastructural updates, often involving larger key sizes and increased processing overhead [10]. Organizations must also ensure crypto-agility, the capability to rapidly adapt cryptographic systems as new standards evolve or vulnerabilities are identified [11].One strategic transition approach involves hybrid cryptographic architectures, which combine conventional and quantum-safe algorithms to provide layered security during this migration period [9]. Moreover, organizations should inventory existing cryptographic assets, conduct quantum risk assessments, pilot PQC deployments, and cultivate a quantum-aware cybersecurity workforce.The threat is amplified by “harvest now, decrypt later” strategies, in which adversaries intercept encrypted data today with the intention of decrypting it once quantum capabilities mature [9]. Particularly sensitive categories — including military communications, governmental archives, personal healthcare records, and intellectual property — require immediate PQC-oriented protection.As the quantum era approaches, coordinated global action is essential to harness quantum innovation while safeguarding digital trust. Through proactive investment in research, standardization efforts, and phased post-quantum deployment, societies can preserve digital sovereignty and security resilience in the face of this technological shift [11].
The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport Layer Security (TLS) and Internet Protocol Security (IPsec) become vulnerable to quantum attacks. This paper presents a structured survey based on a systematic classification of recent literature across AI-enabled networking, Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level integration approaches. The review categorizes existing works into AI-driven network optimization, quantum-resilient security mechanisms, and hybrid AI–QRC architectural frameworks, enabling a unified comparison of design strategies and deployment models. Furthermore, this survey analyzes how Artificial Intelligence (AI) techniques enable autonomous network operations while Quantum-Resilient Cryptography (QRC) approaches provide post-quantum security guarantees, highlighting their complementary roles in next-generation network design. The study further synthesizes key findings for a tightly coupled AI–QRC co-design, which remains an open challenge due to mismatched assumptions in control latency, cryptographic overhead, and protocol adaptability. Additionally, this review emphasizes the need for integrated frameworks that combine intelligence and quantum security to develop adaptive, scalable, and resilient network protocols for the post-quantum era.
Critical infrastructures (CI)-including power grids, financial systems, communication networks, and transportation networks-are essential to societal stability and economic security. As these systems grow increasingly complex and interconnected, they face mounting challenges in scalability, robustness, real-time decision-making, and data privacy. While classical artificial intelligence (AI) and machine learning (ML) techniques have been applied to enhance CI monitoring, optimization, and control, they are often constrained by data heterogeneity, centralized architectures, and limited adaptability. Quantum computing (QC), known for its potential to solve certain computational problems exponentially faster than classical methods, has opened new possibilities in this domain. Quantum artificial intelligence (QAI), which combines quantum computing with AI techniques, offers transformative opportunities, particularly through paradigms such as federated quantum machine learning (FedQML) and quantum reinforcement learning (QRL). These approaches promise privacy-preserving distributed intelligence and dynamic control across CI systems. In this article, we survey recent developments in QAI and explore their applications in critical infrastructure domains. We also highlight key challenges and outline a forward-looking roadmap toward resilient, secure, and quantum-enhanced infrastructure systems.