arXiv (All)
open access
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 (
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage#163174
- Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting#1034404
- Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting#978427
- Structured Quantum Kernels for Chaotic Forecasting#1071156
- Towards unsupervised representation learning for quantum data: quantum models with inference and generation#819824
Record · ID 1202107
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive