End-to-End Formalization of Quantum Error Correction
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Abstract (arXiv)
Quantum error-correcting codes (QECCs) sit between noisy quantum hardware and reliable computation, so the code parameters used in practice must be trustworthy. The single number that summarizes a code's strength is its distance, yet certifying a distance lower bound is NP-hard in general, placing it beyond the reach of pen-and-paper proofs as well as direct proof-assistant scripting. As a result, distance values in the literature come either from non-scaling hand proofs, or from unverified solvers that leave a trust gap exactly where the code is supposed to provide a guarantee. We present Lean-QEC, the first Lean 4 formalization of stabilizer-code theory that delivers end-to-end, machine-checked distance certificates at industrial code sizes. Lean-QEC formalizes the linear algebra of qubit states, the Pauli group, stabilizer codes, the binary symplectic representation, classical coding theory, and the CSS and Bivariate Bicycle families. To break the combinatorial barrier, Lean-QEC translates the distance condition into a Boolean satisfiability formula through a verified reduction. The pipeline scales through a BitVec-flattened encoding that replaces Lean's Matrix representation, and an error-location encoding that reduces the variable count from n to klceil log2 nrceil. With these, we obtain automatically-generated Lean-checked distance proofs for a large range of industrially viable qLDPC codes within the Bivariate Bicycle and Generalized Bicycle families, including [[90, 8, 10]] and [[70, 6, 9]] BB codes, with the formulation scaling up to 144 qubits when performed outside the Lean kernel. The resulting library is reusable and is designed to plug into broader Lean-based efforts toward end-to-end verification of fault-tolerant quantum computation.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2026.
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Citing papers per year: 2026: 4.
4 papers.
- 2026Multi-agent Autoformalization of Tensor Network Theorycited by 2
- 2026Lean-QIT: Towards a Formal Infrastructure for Quantum Information Theorycited by 2
- 2026AxQM: A Textbook-Scale Benchmark for Formal Proof Synthesis in a Library of Finite-Dimensional Quantum Mechanicscited by 1
- 2026When AI meets quantum information: A comprehensive reviewcited by 0
- 2026AxQM: A Textbook-Scale Benchmark for Formal Proof Synthesis in a Library of Finite-Dimensional Quantum Mechanics4 Sep 2026
- 2026Lean-QIT: Towards a Formal Infrastructure for Quantum Information Theory10 Jul 2026
- 2026Multi-agent Autoformalization of Tensor Network Theory8 Jul 2026
- 2026When AI meets quantum information: A comprehensive review1 Jul 2026