Beyond Independent Candidates: Joint Quantum Reasoning for Ambiguous Structured Generation

Structured generation often requires selecting among multiple plausible interpretations of an underspecified input. Existing ambiguity-aware approaches preserve multiple valid outputs during learning, but typically score candidate structures independently, ignoring how competing interpretations agree or conflict with one another. We introduce a hybrid quantum–classical framework that instead reasons jointly over the candidate set. A classical generator produces multiple structured candidates, which are represented as relational graphs and encoded into a shared candidate-indexed quantum state. Pairwise structural disagreements condition parameterized quantum interactions, allowing the probability assigned to each candidate to depend on its relationships with competing hypotheses. Because several interpretations may be simultaneously valid, we further introduce a quantum multi-valid objective that maximizes measurement probability over the valid-candidate subspace rather than a single reference. We additionally derive finite-shot selection-stability conditions based on probability margins between competing interpretations. Experiments on text-to-SQL and text-to-visualization benchmarks show that the proposed framework outperforms classical graph reasoning, parameter-matched joint reasoning, and candidate-wise quantum baselines, reaching 62.5 and 73.4 exact match on nvBench and Spider, respectively. The gains increase with ambiguity, while finite-shot, circuit-depth, and quantum-noise analyses characterize robustness under quantum execution constraints.