AI 中文总结
该研究证明AI状态跟踪任务存在推理阶段量子协调优势，提出边界保持语义编译定理，通过问答、需求审计、稳定器对话等应用展示量子与经典方法的内存及协调分离。
AI 中文摘要
我们证明了特定人工智能状态跟踪任务在推理阶段存在量子协调优势。求解器将语义历史压缩为未来可访问的边界状态，随后回答查询。我们统计通信量B、与实例相关的持久内存M以及本地工作D；经典循环、缓存、工具和重计算均被允许并计入开销。核心结果是边界保持语义编译定理，它将有限单向、流式或自适应因果任务映射为语义AI接口，同时保留事件顺序和对过去输入的访问。经典边界状态下界与量子内存上界可转移至显式编译器开销，且与有限精度循环架构无关。该定理有两个具有经典语义的应用：匹配实体概要问答继承了O(log N)量子比特与Ω(√N)经典边界比特之间的隐藏匹配分离；持续需求审计继承了Max-kSAT流式分离：循环求解器使用O(log⁵n log(1/δ))量子比特和多对数经典工作空间即可获得0.7172的近似比，而每一个达到该近似比的经典单遍有限信息求解器都需要Ω(√n)的协调宽度。作为量子原生编译器测试，稳定器潜在状态对话使用n个量子比特，而每一个精确有限状态经典因果在线实现都满足B+M ≥ (1/2)n² + (3/2 - log₂3)n + O(1)。源协议、流式算法和稳定器见证均被引入；新结果是它们与架构无关的语义转移。这些是内存和协调上的分离，而非当前语言模型的运行时间或经验优势。稳定器结果假设精确模拟和理想无噪声量子内存。
英文摘要
We prove inference-time quantum coordination advantages for specified AI state-tracking tasks. A solver compresses semantic history into a future-accessible boundary state and later answers a query. We count communication $B$, persistent instance-dependent memory $M$, and local work $D$; classical recurrence, caches, tools, and recomputation are allowed and charged. The central result is a boundary-preserving semantic-compilation theorem. It maps a finite one-way, streaming, or adaptive causal task into a semantic AI interface while preserving event order and access to past input. Classical boundary-state lower bounds and quantum-memory upper bounds transfer up to explicit compiler overhead, independently of the finite-precision recurrent architecture. Two applications have classical semantics. Matched-entity synopsis QA inherits the hidden-matching separation between $O(\log N)$ qubits and $Ω(\sqrt{N})$ classical boundary bits. Continual requirements auditing inherits a Max-$k$SAT streaming separation: a recurrent solver uses $O(\log^5 n\log(1/δ))$ qubits and polylogarithmic classical workspace to obtain a $0.7172$-approximation, whereas every classical one-pass finite-information solver attaining that ratio requires $Ω(\sqrt{n})$ coordination width. As a quantum-native compiler test, a stabilizer latent-state dialogue uses $n$ qubits, while every exact finite-state classical causal online realization satisfies $B+M \ge \frac{1}{2}n^2+(\frac{3}{2}-\log_2 3)n+O(1)$. The source protocols, streaming algorithms, and stabilizer witness are imported; the new result is their architecture-independent semantic transfer. These are memory and coordination separations, not runtime or empirical advantages for present-day language models. The stabilizer result assumes exact simulation and ideal noiseless quantum memory.
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