Quantum Threat Mitigation in Blockchain AI Architectures

Abstract

Blockchain AI architectures stand at the nexus of distributed consensus and machine-driven intelligence, offering transformative applications across finance, supply chain, healthcare, and beyond. However, the emergence of scalable quantum computers threatens foundational cryptographic primitives—most notably, elliptic-curve and RSA-based schemes—that secure transaction integrity, key exchange, and smart-contract execution. This manuscript presents a comprehensive evaluation of quantum threat mitigation strategies tailored for blockchain AI systems. We examine three distinct approaches: (1) adoption of post-quantum cryptographic algorithms (lattice-based signatures and hash-based KEMs) that resist Shor’s algorithm; (2) integration of hybrid quantum-classical key distribution leveraging quantum key distribution (QKD) combined with classical post-quantum algorithms; and (3) deployment of trusted execution environments (TEEs) to isolate critical key operations from both classical and quantum side-channel attacks. Our mixed-methods methodology includes simulation on a 50-node blockchain AI testbed, processing 240,000 transactions over 24 hours, and statistical analysis of throughput, latency, and key-exchange performance. The results demonstrate that lattice-based schemes maintain 128-bit quantum security with a moderate performance overhead (~27 % throughput reduction, ~20 % increased latency), while hybrid QKD delivers information-theoretic security but incurs significant latency penalties (115 % higher than baseline). TEEs effectively mitigate side-channels with negligible additional overhead. We conclude by offering practical guidelines for phased deployment—beginning with immediate migration to lattice-based cryptography, followed by pilot QKD integration for high-security use cases—and outline future research directions.