Quantum Computing and Artificial Intelligence Security

Shaukat Ali, Francisco Chicano, Alberto Moraglio

Samuel Yen-Chi Chen, Kuan‐Cheng Chen

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.

Merve Güllü, Sedat Çimen, Deniz Karhan

The sixth generation (6G) of wireless networks is expected to surpass the limitations of 5G by enabling terabit-level data rates, sub-millisecond latency, and ultra-dense connectivity through the integration of space-air-land-sea infrastructures and AI-driven autonomous management. However, such advancements pose new challenges in computational scalability and security against quantum-enabled threats. Quantum Artificial Intelligence (QAI), combining quantum computing principles with AI, offers exponential computational efficiency, enhanced model capacity, and resilient cryptographic solutions. This paper reviews QAI-based protocols for 6G across three domains: security, focusing on Quantum Key Distribution (QKD) and post-quantum cryptography; computing, including quantumenhanced edge intelligence and hybrid optimization; and data processing, with emphasis on terahertz (THz) channel modeling, holographic content, and real-time decision systems. The convergence of 6G and QAI is shown to mark a paradigm shift toward secure, scalable, and intelligent communication infrastructures of the future.

Shaukat Ali, Francisco Chicano, Alberto Moraglio

Sulekh Kumar, Md Shamsher Alam, Jeevan Kumar +3 more

Deepika Bhatia, Pranav Bhardwaj, Jayati Ahuja

Applying quantum artificial intelligence (QAI) to protect healthcare systems includes several technologies that improve data security and protect against threats. The chapter emphasizes the need for advanced cybersecurity techniques in healthcare, integrating QAI to protect sensitive data. Traditional defense frameworks are insufficient, and QAI, a combination of quantum computing and artificial intelligence (AI), is needed to address the complexities of cyber threats and establish enduring cybersecurity frameworks. The chapter describes the cybersecurity problems of the healthcare sector. Digital transformation has revolutionized the healthcare sector by introducing electronic health records, connected medical devices, and telemedicine which now provide a bigger target for cyber adversaries. The delicacy of health data is the reason why it is the major target of ransomware, data breaches, and other hacking techniques. As the threats grow more complicated, traditional security solutions may become less effective. This chapter presents QAI as a novel technique for taking care of these issues using the incredible computational power of quantum computing combined with the pattern recognition and decision-making skills of AI. Furthermore, the chapter reviews the field of quantum resistance, which is defined as the capability of algorithms to deliver secure encryption even in the face of quantum computers; under this context, the chapter presents lattice-based and hash-based cryptography as critical to protecting healthcare data from future quantum cyberattacks. These encryption techniques protect against threats from quantum computing and most of today’s common encryption methods such as the Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are vulnerable to breakage by quantum machines.