Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
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 t...
Description / Details
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 , persistent instance-dependent memory , and local work ; 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 qubits and classical boundary bits. Continual requirements auditing inherits a Max-SAT streaming separation: a recurrent solver uses qubits and polylogarithmic classical workspace to obtain a -approximation, whereas every classical one-pass finite-information solver attaining that ratio requires coordination width. As a quantum-native compiler test, a stabilizer latent-state dialogue uses qubits, while every exact finite-state classical causal online realization satisfies . 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.
Source: arXiv:2608.11066v1 - http://arxiv.org/abs/2608.11066v1 PDF: https://arxiv.org/pdf/2608.11066v1 Original Link: http://arxiv.org/abs/2608.11066v1
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Aug 12, 2026
Artificial Intelligence
AI
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