这篇论文用数学证明了量子在状态追踪任务上的内存优势，不是跑分，是理论分离，适合想了解量子AI理论底层的朋友。
该论文证明了在特定AI状态追踪任务中，推理时量子协调优势的存在。研究者提出边界保持语义编译定理，将有限单向、流式或自适应因果任务映射到语义AI接口，同时保留事件顺序和过去输入访问。在匹配实体概要问答中，量子方法仅需O(log N)量子比特，而经典边界状态需要Ω(√N)比特。持续需求审计中，量子递归求解器用O(log^5 n log(1/δ))量子比特获得0.7172近似，经典单遍求解器需Ω(√n)协调宽度。稳定子潜在状态对话使用n量子比特，经典精确有限状态实现需B+M≥1/2n^2+(3/2-log2 3)n+O(1)。
Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
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.