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[2602.16554] MerLean: An Agentic Framework for Autoformalization in Quantum Computation
Summary
MerLean introduces an automated framework for autoformalization in quantum computation, converting mathematical statements into verified Lean code and back to LaTeX, streamlining the verification process.
Why It Matters
This framework addresses the growing need for rigorous verification in quantum computing research, providing a scalable solution that enhances the reliability of theoretical work and supports machine-verified peer review. Its implications extend beyond quantum computing to other fields requiring formal verification.
Key Takeaways
- MerLean automates the extraction and formalization of mathematical statements.
- It successfully reduces the verification burden to newly introduced definitions and axioms.
- The framework can be applied to various fields in mathematics and theoretical physics.
- MerLean generates high-quality synthetic data for training reasoning models.
- It serves as a practical tool for machine-verified peer review in quantum research.
Computer Science > Logic in Computer Science arXiv:2602.16554 (cs) [Submitted on 18 Feb 2026] Title:MerLean: An Agentic Framework for Autoformalization in Quantum Computation Authors:Yuanjie Ren, Jinzheng Li, Yidi Qi View a PDF of the paper titled MerLean: An Agentic Framework for Autoformalization in Quantum Computation, by Yuanjie Ren and 2 other authors View PDF HTML (experimental) 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. Subjects: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Quantum Physics (quant-ph) Cite as: arXiv:26...
