mlsanityData to 7 Oct 2026

Co-Designing Quantum Codes with Transversal Diagonal Gates via Multi-Agent Systems

arXiv 2510.20728 ↗First posted 23 Oct 2025quant-ph cs.AI cs.CLcited by 3

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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

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  5. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  6. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  8. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
  9. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 763
  10. 2023Cognitive Architectures for Language Agentscited by 422
  11. 2024SciCode: A Research Coding Benchmark Curated by Scientistscited by 172
  12. 2025Can Theoretical Physics Research Benefit from Language Agents?cited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.