arXiv (All)
open access
Structure-Aware Variance Reduction for Unbiased Randomized Hamiltonian Simulation
Randomized Hamiltonian simulation methods are often governed by a trade-off between systematic bias and sampling overhead. We study how classical variance-reduction techniques can be applied to such methods without changing their mean channel, and therefore without introducing additional bias. As a
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
- Scalable Boltzmann generators for equilibrium sampling of large-scale materials.#266883
- On the Geometric Ergodicity of Hamiltonian Monte Carlo#984769
- Efficient Monte Carlo sampling of metastable systems using nonlocal collective variable updates#194417
- Dimensional hyperreduction of nonlinear finite element models via empirical cubature with manifold-adaptive weights#925435
- Dimensional hyperreduction of nonlinear finite element models via empirical cubature with manifold-adaptive weights#921199
Record · ID 1034749
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