QuantumMind：面向量子计算加速分析的基于约束的智能体推理框架
QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing
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中文总结 AI 辅助
QuantumMind是用于量子加速分析的可审计智能体工作流，经582个任务评估，其ODS等指标优于基线，可有效提升量子加速假设筛选的准确性与效率。
中文摘要 AI 辅助
要证明有意义的量子加速，不仅需要将经典问题与熟悉的量子原语匹配，还需满足：主张需保留任务、遵循访问和输出模型、明确所需前提条件、处于可辩护的复杂度范围内。我们提出QuantumMind，这是一种可审计的智能体工作流，用于生成和保守筛选量子加速假设。该工作流采用固定序列的类型化、角色专业化操作，将公共任务形式化、分析结构与经典瓶颈、匹配关联量子原语与障碍的源链接注册表、构建带范围的候选方案。确定性十项检查验证器给出权威裁决；完成状态被编译为量子加速证据图，并通过仅向下的研究筛选（无法强化决策）。我们在582个相同的开放发现任务上，针对7种任务适配的提示与智能体控制方法评估QuantumMind。在冻结开放发现分数（ODS）下，QuantumMind的平均ODS为53.1，超出最强基线17.3分（相对提升48.2%），在582个配对任务中对该基线胜出355次；其任务的图审计通过率为99.8%，而最强基线仅为43.6%，且在所有7个任务族中排名第一。结果表明，类型化状态转换与确定性证据控制的贡献超出流畅生成本身。
英文摘要
Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope. We present QuantumMind, an auditable agentic workflow for generating and conservatively screening quantum-acceleration hypotheses. A fixed sequence of typed, role-specialized actions formalizes the public task, analyzes structure and classical bottlenecks, matches a source-linked registry of quantum primitives and barriers, and constructs a scoped candidate scheme. A deterministic ten-check validator assigns the authoritative verdict; completed states are compiled into a Quantum Acceleration Evidence Graph and passed through a downward-only research screen that cannot strengthen the decision. We evaluate QuantumMind against seven task-adapted prompting and agentic controls on 582 identical open-discovery tasks. Under the frozen Open-Discovery Score (ODS), QuantumMind obtains 53.1 mean ODS, exceeding the strongest baseline by 17.3 points (48.2% relative), and wins 355 of 582 paired tasks against that baseline. It passes the graph audit on 99.8% of tasks, compared with 43.6% for the strongest baseline, and ranks first in all seven task families. The results indicate that typed state transitions and deterministic evidence control contribute beyond fluent generation alone.
