Quantum-Inspired Optimization Algorithms for Scalable AI Scheduling Systems
Keywords:
quantum-inspired optimization, AI scheduling, scalability, large-scale systems, socio-technical infrastructure, fairness, sustainabilityAbstract
The rapid expansion of artificial intelligence (AI) workloads across cloud computing, edge environments, and cyber-physical systems has exposed fundamental limitations in traditional scheduling frameworks, which often struggle to balance scalability, real-time responsiveness, and resource efficiency. Quantum-inspired optimization algorithms, which draw on principles from quantum mechanics without requiring quantum hardware, offer a promising alternative for designing scalable AI scheduling systems. This paper examines the structural trade-offs, architectural implications, and socio-technical considerations that arise when integrating such algorithms into large-scale scheduling infrastructures. We analyze how quantum-inspired methods, including simulated annealing, quantum annealing emulation, and variational approaches, can be adapted to combinatorial scheduling problems that are inherently NP-hard. The discussion extends to governance and policy dimensions, including fairness in resource allocation, robustness under adversarial conditions, and the environmental sustainability of high-throughput scheduling systems. By situating quantum-inspired optimization within the broader context of AI infrastructure, we highlight critical challenges in deployment, interoperability, and regulation. Cross-domain comparisons with classical heuristics and deep reinforcement learning methods reveal that quantum-inspired techniques can achieve near-optimal solutions with reduced computational overhead in specific regimes, yet they demand careful calibration of hyperparameters and integration with existing orchestration layers. The paper concludes with a forward-looking agenda for research that bridges algorithmic innovation with responsible system design, emphasizing the need for transparent benchmarks, standardized interfaces, and adaptive governance frameworks. Our analysis aims to inform both researchers and practitioners seeking to harness quantum-inspired optimization for sustainable, equitable, and resilient AI scheduling systems.
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