arXiv (All)
open access
Random dimension reduction and learning symmetric properties of quantum states
We describe a procedure that simultaneously reduces the dimensions of many, potentially distinct quantum states while preserving properties invariant under the tensor power action of an isometry. This serves as a black-box method to replace the dimension with the maximum rank in the sample complexit
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
- Dimensional reduction in quantum gravity#499077
- Optimal entanglement criteria from trace invariants#1077400
- Quantum Tensor Network Learning with DMRG#467140
- Sampling isometric tensor network states with monitored quantum circuits#793874
- Sampling isometric tensor network states with monitored quantum circuits#789855
Record · ID 1187459
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