Post-Quantum Cryptography and Quantum Machine Learning for Resilient Encryption in AI-Driven Cybersecurity
Krishna Kumar, Sathea Sree S, Raj Laxmi · 2025
The rapid evolution of quantum computing poses a significant challenge to traditional encryption systems, with the potential to compromise the security of sensitive digital infrastructures. Post-Quantum Cryptography (PQC) has emerged as a vital field, aiming to develop encryption algorithms resilient to quantum attacks. Simultaneously, Quantum Machine Learning (QML) is revolutionizing the way machine learning models process data, offering new avenues for enhancing cybersecurity measures. This chapter explores the integration of PQC and QML to create robust, future-proof encryption systems capable of adapting to the evolving threat landscape. By examining hybrid models that combine the quantum resistance of PQC with the adaptability and efficiency of QML, this work highlights the potential for creating scalable and efficient cryptographic frameworks. The challenges and opportunities presented by the intersection of PQC and QML are discussed, with a focus on resource-constrained environments where computational power and memory are limited. Through this analysis, the chapter offers a comprehensive roadmap for advancing AI-driven, quantum-resistant cybersecurity solutions, addressing both theoretical advancements and practical implementation challenges.