arXiv (All)
open access
Near-Optimal Quantum Lower Bounds for Convex Optimization via Fourier Rank
We establish a near-linear quantum query lower bound for high-accuracy convex optimization over an explicit family of $n$-dimensional ellipsoids. We focus on linear optimization with an explicitly given objective, where the feasible set is accessed through a membership oracle. We show that any algor
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
- A dual-active-set algorithm for positive semi-definite quadratic programming#429998
- Robust Iterative Method for Symmetric Quantum Signal Processing in All Parameter Regimes#642074
- Quantum advantage bounds for a multipartite Gaussian battery#629850
- Quantum advantage bounds for a multipartite Gaussian battery#627988
- Lecture Notes on Applied Optimization#1085070
Record · ID 986644
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