mlsanityData to 14 Oct 2026

A Machine-Verified Proof of a Quantum-Optimization Conjecture

arXiv 2606.29687 ↗First posted 29 Jun 2026quant-ph cs.AI cs.LGcited by 3

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Abstract (arXiv)

We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture that depth-p Quantum Approximate Optimization Algorithm (QAOA) on the ring of disagrees attains approximation ratio (2p+1)/(2p+2) exactly. We found the proof using a large language model, Claude Fable 5, and verified its correctness end-to-end by the Lean 4 proof assistant. Our methodology includes several ingredients: building on a substantial Lean library of quantum information, we formalized the QAOA components and the known parts of the problem, and reduced the conjecture to a single open mathematical statement. The model was then handed the library and our agentic toolkit, and tasked with closing that gap by constructing a proof in Lean. The resulting process is a feedback loop between the model's natural-language reasoning and Lean's mechanical verification, which converged to a machine-verified proof. Human verification is required only for the structural scaffolding - that the formal statement faithfully encodes the intended claim - while the proof itself is supplied by the model and certified mechanically by Lean. The proof is nevertheless striking - the model uncovered a hidden dynamical symmetry of the problem and exploited it, borrowing tools and machinery from an adjacent field to turn a hard existence problem into an explicit construction. This work paves the way for resolving open conjectures in quantum information science and beyond.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 29 Jun 2026.

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2 of 11 references.

References · 2

  1. 2025DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decompositioncited by 287
  2. 2025Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physicscited by 31

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Citing papers per year: 2026: 3.

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  1. 2026Quantum automated theorem proving
  2. 2023Classical Verification of Quantum Learning
  3. 2026Proving olympiad geometry theorems on a superconducting quantum processor
  4. 2026SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis
  5. 2026MerLean: An Agentic Framework for Autoformalization in Quantum Computation
  6. 2026QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing
  7. 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
  8. 2025Quantum Data Encoding and Variational Algorithms: A Framework for Hybrid Quantum Classical Machine Learning