---
格式版本: 2
标题: "Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory"
原文链接: "https://arxiv.org/abs/2608.11066"
发布日期: "2026-08-11"
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入库时间: "2026-08-12T03:28:17.445Z"
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去重键: "https://arxiv.org/abs/2608.11066"
---

## Quantum Physics

## Title:Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory

Authors:[Ming Yang](https://arxiv.org/search/quant-ph?searchtype=author&query=Yang,+M)

[View PDF](https://arxiv.org/pdf/2608.11066) [HTML (experimental)](https://arxiv.org/html/2608.11066v1)

> Abstract: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 $\Omega(\sqrt{N})$ classical boundary bits. Continual requirements auditing inherits a Max-$k$SAT streaming separation: a recurrent solver uses $O(\log^5 n\log(1/\delta))$ qubits and polylogarithmic classical workspace to obtain a $0.7172$-approximation, whereas every classical one-pass finite-information solver attaining that ratio requires $\Omega(\sqrt{n})$ 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.

| Comments: |  |
| --- | --- |
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC) |
| Cite as: | [arXiv:2608.11066](https://arxiv.org/abs/2608.11066) \[quant-ph\] |
|  | (or [arXiv:2608.11066v1](https://arxiv.org/abs/2608.11066v1) \[quant-ph\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11066](https://doi.org/10.48550/arXiv.2608.11066) |

## Submission history

From: Ming Yang \[[view email](https://arxiv.org/show-email/ec0036d2/2608.11066)\]  
**\[v1\]** Tue, 11 Aug 2026 15:32:15 UTC (27 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11066) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
