Co-Designing Quantum Codes with Transversal Diagonal Gates via Multi-Agent Systems
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Abstract (arXiv)
Exact scientific discovery requires more than heuristic search: candidate constructions must be turned into exact objects and checked independently. We address this gap by extending TeXRA with an independent Lean 4 verification layer, turning it into a human-guided multi-agent platform for exact scientific discovery. The platform couples symbolic synthesis, combinatorial and linear-programming search, exact reconstruction of numerical candidates, and formal verification in Lean. We apply this platform to nonadditive quantum error-correcting codes with prescribed transversal diagonal gates within the subset-sum linear-programming (SSLP) framework. In the distance-2 regime where logical states occupy distinct residue classes, the platform yields a Lean-certified catalogue of 14,116 codes for K∈2,3,4 and up to six physical qubits, realizing cyclic logical orders 2 through 18, from which we extract closed-form infinite families. We also construct a residue-degenerate ((6,4,2)) code implementing the logical controlled-phase gate diag(1,1,1,i). At distance 3, we resolve the transversal-T problem for ((7,2,3)) codes within the complementary binary-dihedral BD16 setting: among the 12 candidates surviving the SSLP filters, 10 admit exact realizations and 2 are excluded by no-go proofs. All accepted constructions, families, and no-go results are formalized and checked in Lean, illustrating how AI-assisted workflows can bridge search, exact reconstruction, and formal proof in the physical sciences.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 23 Oct 2025.
Builds on
12 of 45 references.
References · 12
- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
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- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
- 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 763
- 2023Cognitive Architectures for Language Agentscited by 422
- 2024SciCode: A Research Coding Benchmark Curated by Scientistscited by 172
- 2025Can Theoretical Physics Research Benefit from Language Agents?cited by 9
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Citing papers per year: 2026: 3.
3 papers.