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MerLean: An Agentic Framework for Autoformalization in Quantum Computation
arXiv
Authors: Yuanjie Ren, Jinzheng Li, Yidi Qi
Year
2026
Paper ID
15785
Status
Preprint
Abstract Read
~2 min
Abstract Words
127
Citations
N/A
Abstract
We introduce MerLean, a fully automated agentic framework for autoformalization in quantum computation. MerLean extracts mathematical statements from \LaTeX{} source files, formalizes them into verified Lean 4 code built on Mathlib, and translates the result back into human-readable \LaTeX{} for semantic review. We evaluate MerLean on three theoretical quantum computing papers producing 2,050 Lean declarations from 114 statements in total. MerLean achieves end-to-end formalization on all three papers, reducing the verification burden to only the newly introduced definitions and axioms. Our results demonstrate that agentic autoformalization can scale to frontier research, offering both a practical tool for machine-verified peer review and a scalable engine for mining high-quality synthetic data to train future reasoning models. Our approach can also be generalized to any other rigorous research in mathematics and theoretical physics.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We introduce MerLean, a fully automated agentic framework for autoformalization in quantum computation.
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