Agentic AI Meets Quantum Computing: Frontiers of Digital Futures

Islam, Ray

Thomas Kristan

Pethuru Raj, Ramakrishnan Dhaya

This chapter describes the mesmerizing journey of artificial intelligence (AI) artificial intelligence (AI) , from classical systems to modern cutting-edge innovation cutting-edge innovation s. The era begins with deep roots in the historical milestones of AI; it all extends out to the inception of the quantum era quantum era – when quantum mechanics and quantum computation meet the already ambitious world of AI to overcome technological boundaries. quantum computing (QC) Quantum computing is not only the next frontier in computing; it is the next frontier for AI experts to explore. Particular emphasis is placed on generative adversarial networks (GANs) – an innovative method to ML machine learning (ML) that employs adversarial mechanisms to produce astonishingly realistic data. The section allows an analysis on the intricacies of training the neural networks of GANs and also an exploration of quantum generative adversarial network (QGAN), which has the potential for improving generative performance through quantum advantage. Other topics also covered include quantum computing, AI, and the metaphorical phase of machine intelligence, the balance between human creativity and machine intelligence, with quantum computing’s parallel processing and algorithm knowledge being a complementary force. Collectively, they provide a shimmering and soon-to-be-mating pair of building blocks for the next generation of applications that will transfigure enterprises and set human beings loose – evolution and revolution in the era of digital transformation digital transformation .

Fatma Foad Ashrif, Rami Ahmad, Faiza A. M. Mansour

Mohammed M. Al-Mhadawi, Qahtan M. Yas

Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography. Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed. Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study. Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.

Maha A. Metawei, Hesham E. ElDeeb, Salwa M. Nassar +1 more