Quantum Statistical Memory Advantage Reveals Predictive Structure in Chaotic Invariant Measures
Long-horizon prediction of a chaotic system is governed by fidelity to its invariant measure, and which parts of that measure a learned predictor needs is a physical question not known in advance. We establish a quantum statistical memory advantage for interrogating it. A quantum statistical prior (Q-Prior), trained once on classical data, stores an efficiently preparable $k$-point marginal in polynomially many circuit parameters against exponentially many for explicit tabulation. Collective Bell measurements on two copies then estimate any post hoc Pauli-expectation magnitude at a copy cost independent of register size, with a worst-case exponential separation from single-copy protocols....
| Authors | Maida Wang, Xiao Xue, Minh Chung et al. |
|---|---|
| Primary category | quant-ph |
| Published | 11 June 2026 |
| Last updated at source | 5 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.