arXiv (All)
open access
Learned-projector QAOA for hierarchical optimization
Many optimization problems reveal inexpensive structural information before requiring costly final evaluation, whereas the conventional quantum approximate optimization algorithm (QAOA) applies a single aggregate objective throughout. We introduce the learned-projector quantum alternating operator 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
- Learning parameter curves in feedback-based quantum optimization algorithms#1144750
- Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization#1156210
- Managing Iterative Hybrid Quantum-Classical Optimization as a First-Class Scientific Workflow#1104731
- Super-Quadratic Quantum Speedups for Combinatorial Optimization via Tilted Walks#1154357
- An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory.#136047
Record · ID 1076544
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