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

arXiv: https://arxiv.org/abs/2608.11066 · HTML: https://mlsanity.com/paper/2608.11066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Illusion of State in State-Space Models](https://mlsanity.com/paper/2404.08819/) (2024)
- [On Limitations of the Transformer Architecture](https://mlsanity.com/paper/2402.08164/) (2024)
- [Enhancing Generative Models via Quantum Correlations](https://mlsanity.com/paper/2101.08354/) (2021)
- [(How) Do Language Models Track State?](https://mlsanity.com/paper/2503.02854/) (2025)
- [Positive semidefinite rank](https://mlsanity.com/paper/1407.4095/) (2014)
- [On the Failure of Latent State Persistence in Large Language Models](https://mlsanity.com/paper/2505.10571/) (2025)
- [Generalization Metrics for Practical Quantum Advantage in Generative Models](https://mlsanity.com/paper/2201.08770/) (2022)
- [The Topological Trouble With Transformers](https://mlsanity.com/paper/2604.17121/) (2026)
- [Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model](https://mlsanity.com/paper/2311.14123/) (2023)
- [Exponential Quantum Space Advantage for Approximating Max-kSAT in the Streaming Setting](https://mlsanity.com/paper/2606.05366/) (2026)

## Abstract (arXiv)

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(√N) classical boundary bits. Continual requirements auditing inherits a Max-kSAT streaming separation: a recurrent solver uses O(log5 nlog(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 Omega(√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 ≥ frac12n2+(frac32-log2 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.