Navigating optimization and security challenges in the AI-driven digital economy: from classical limits to quantum solutions

Abstract

The rapid growth of the AI-driven digital economy has intensified the demand for scalable optimization techniques and secure computational frameworks across industries such as logistics, finance, healthcare, and autonomous systems. Classical computational frameworks are becoming inadequate for addressing the inherent combinatorial complexity of AI processes and for maintaining robust cryptographic protections suitable for decentralized and sensitive data environments. This paper explores how emerging quantum computing paradigms, particularly the quantum approximate optimization algorithm and quantum-state-based cryptographic protocols, can provide effective solutions to these dual challenges. As fully fault-tolerant quantum systems remain on the horizon, hybrid quantum-classical architectures provide a practical, near-term solution. These systems embed quantum modules into classical AI pipelines, enabling enhanced optimization capabilities and quantum-resilient communication within the constraints of current noisy intermediate-scale quantum devices. We discuss a potential hybrid architecture intended to support complex decision-making and secure data exchange in practical settings. The study includes comparative analyses of classical, post-quantum, and quantum-state-based techniques and evaluates their applicability across key sectors. Rather than replacing existing systems, quantum methods are positioned as complementary technologies, offering domain-specific advantages in computational efficiency, data security, and decentralized functionality, which are essential capabilities for the future AI-enabled digital infrastructure.