Tuning CPU-only Qwen3-30B inference with an IBM Quantum sampling loop (github.com)

A recent project has introduced a hybrid quantum optimization approach to enhance the inference capabilities of the Qwen3-30B Mixture-of-Experts (MoE) large language model (LLM) on CPU-only legacy hardware, specifically a 2017 Intel MacBook Air. This development is significant for the AI/ML community as it demonstrates how quantum computing techniques can improve hyperparameter tuning and performance of traditional computing systems, pushing the boundaries of legacy hardware capabilities. The project recorded a remarkable enhancement from about 0.09 to 14.03 tokens per second (tok/s) in model generation speeds by integrating an IBM Quantum sampling loop, which helps refine candidate selections through a synchronized research loop involving Codex for generating and testing configurations. The method showcases how quantum sampling can effectively assist in optimizing the performance of LLMs without requiring quantum processing units (QPUs) for the models themselves. Instead, the MacBook remains responsible for executing the LLM inference, while candidate configurations are evaluated and optimized through a quantum-enhanced feedback loop. This collaborative research environment, leveraging both classical computing and quantum principles, provides a novel framework for improving LLM performance, potentially paving the way for more advanced autoresearch techniques in AI. The corresponding repository includes tools, benchmarks, and experimental workflows, promoting replication and further exploration by the AI research community.