LLMs can discover domain-specific algorithm improvements by searching over heuristic families rather than predicting solutions directly—here achieving 71% win rate on quantum circuit optimization by aligning variable ordering with actual quantum cost rather than proxy metrics.
This paper uses large language models to discover better algorithms for ordering variables in quantum circuit design. The key challenge is that quantum circuits implementing Boolean functions need optimal variable orderings to minimize quantum cost, but existing heuristics optimize for the wrong metric.