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.
Author(s) Details
Amit Taneja
Vellore Institute of Technology, Tamil Nadu, India.
Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-99-0
