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A researcher-in-the-loop pipeline combining LLM-guided program evolution, exact evaluation, and Lean formal proof checking discovers a new family of entanglement-assisted quantum error-correcting codes, closing 54 gaps in archived EAQECC tables with 115 verified records and 10 certified nonexistence results.
The paper presents a reinforcement learning method with importance sampling to optimize syndrome extraction in quantum error correction, reducing logical error rates by up to 97.8% compared to state-of-the-art tools.