arXiv — cs.AI preprintsInternational21 October 2026
Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Heisenberg Antiferromagnets
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2602.04943v4 Announce Type: replace-cross Abstract: We study the computational complexity of learning the ground state phase structure of Heisenberg antiferromagnets. Representing Hilbert space as a weighted graph, the variational energy defines a weighted XY model that, for $\mathbb{Z}_2$ phases, reduces to a classical antiferromagnetic Ising model on that graph. For fixed amplitudes, reconstructing the signs of the ground state wavefunction thus reduces to a weighted Max-Cut instance. This links sign optimization of wavefunctions directly to combinatorial optimization.
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training21 October 2026
- Large language model and reinforcement learning based autonomous construction and progress updates21 October 2026
- Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models21 October 2026
- MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring21 October 2026
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution21 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.