# Unsupervised Discovery of Intermediate Phase Order in the Frustrated J1-J2 Heisenberg Model via Prometheus Framework

arXiv: https://arxiv.org/abs/2602.21468 · HTML: https://mlsanity.com/paper/2602.21468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [An exact mapping between the Variational Renormalization Group and Deep Learning](https://mlsanity.com/paper/1410.3831/) (2014)
- [Discovering Phase Transitions with Unsupervised Learning](https://mlsanity.com/paper/1606.00318/) (2016)
- [Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders](https://mlsanity.com/paper/1703.02435/) (2017)
- [Mutual Information, Neural Networks and the Renormalization Group](https://mlsanity.com/paper/1704.06279/) (2017)
- [Machine Learning Topological Invariants with Neural Networks](https://mlsanity.com/paper/1708.09401/) (2017)

## Abstract (arXiv)

The spin-1/2 J1-J2 Heisenberg model on the square lattice exhibits a debated intermediate phase between N'eel antiferromagnetic and stripe ordered regimes, with competing theories proposing plaquette valence bond, nematic, and quantum spin liquid ground states. We apply the Prometheus variational autoencoder framework -- previously applied to classical (2D, 3D Ising) and quantum (disordered transverse field Ising) phase transitions -- to systematically explore the J1-J2 phase diagram using a multi-scale approach. For L=4, we employ exact diagonalization with full wavefunction analysis via quantum-aware VAE. For larger systems (L=6, 8), we introduce a reduced density matrix (RDM) based methodology using DMRG ground states, enabling scaling beyond the exponential barrier of full Hilbert space representation. Through dense parameter scans of J2/J1 ∈ [0, 1] and comprehensive latent space analysis, we identify the structure factor S(π,π) and S(π,0) as the dominant order parameters discovered by the VAE, with correlations exceeding |r| > 0.97. The RDM-VAE approach successfully captures the N'eel-to-stripe crossover near J2/J1 ≈ 0.5--0.6, demonstrating that local quantum correlations encoded in reduced density matrices contain sufficient information for unsupervised phase discovery. This work establishes a scalable pathway for applying machine learning to frustrated quantum systems where full wavefunction access is computationally prohibitive.