Quantum-Enhanced Carbon-Aware Scheduling for Large Language Model Inference

As the computational demand for Large Language Models (LLMs) surges, minimizing the carbon footprint of inference has become a critical challenge. While classical schedulers optimize for throughput, they often neglect the spatial and temporal variance of grid carbon intensity. This paper presents a Hybrid Quantum-Classical (HQC) framework utilizing the Quantum Approximate Optimization Algorithm (QAOA) to solve the layer-to-hardware mapping problem with the explicit objective of minimizing gCO$_2$e emissions. We benchmark our QAOA optimizer against classical Brute Force and Genetic Algorithms across static, dynamic, and noisy environments. Our results demonstrate that QAOA achieves near-perfect optimality (gap