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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Ming Yang
• Venue: arXiv: quant-ph
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.11066v1
• PDF: https://arxiv.org/pdf/2608.11066v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper presents a proof of quantum coordination advantages in AI state-tracking tasks, specifically in tasks involving semantic compilation and latent memory. It demonstrates that quantum computers can compress semantic history into a future-accessible boundary state, which is used to answer queries. The paper also discusses two applications, one related to matching entities in questions and answers, and another for continual requirements auditing.

What the Document Says

• The authors prove quantum coordination advantages in AI state-tracking tasks.
• A solver compresses semantic history into a future-accessible boundary state.
• 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.
• Classical boundary-state lower bounds and quantum-memory upper bounds transfer up to explicit compiler overhead.
• Two applications have classical semantics: matched-entity synopsis QA and continual requirements auditing.
• The paper discusses the hidden-matching separation between O(log N) qubits and Omega(sqrt(N)) classical boundary bits in matched-entity synopsis QA.
• In continual requirements auditing, a recurrent solver uses O(log^5 n log(1/δ)) qubits and polylogarithmic classical workspace to obtain a 0.7172-approximation.
• Every classical one-pass finite-information solver requiring that ratio needs Omega(sqrt(n)) coordination width.
• A stabilizer latent-state dialogue uses n qubits, while every exact finite-state classical causal online realization satisfies B+M >= (1/2)n^2+(3/2-log_2 3)n+O(1).

Key Concepts Mentioned

• Quantum coordination advantages
• AI state-tracking tasks
• Semantic compilation
• Latent memory
• Matched-entity synopsis QA
• Continual requirements auditing
• Stabilizer latent-state dialogue
• Boundary-preserving semantic-compilation theorem

Claims Made by the Document

• Quantum coordination advantages exist in AI state-tracking tasks.
• Matched-entity synopsis QA has a separation between O(log N) qubits and Omega(sqrt(N)) classical boundary bits.
• Continual requirements auditing has a Max-$k$SAT streaming separation.

Evidence Used

• The paper provides a proof of quantum coordination advantages.
• The text discusses the hidden-matching separation in matched-entity synopsis QA.
• In continual requirements auditing, the text mentions a Max-$k$SAT streaming separation.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.