量子协调优势在AI状态追踪任务中的语义编译与潜在记忆
Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
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.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include 量子计算, 状态追踪, 语义编译, arxiv.
Source attribution: curated from arXiv cs.AI (@Ming Yang).
