Work

Machine learning the strong disorder renormalization group method for disordered quantum spin chains

Year: 2026

Type: article

Abstract: We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalization group (SDRG) method. The system consists of S = 1 / 2 spins coupled by antiferromagnetic power-law interactions with decay exponent α at random positions on a one-dimensional chain. Using SDRG as a physics-informed teacher, we compare a Random Forest classifier as a classical baseline with a graph neural network (GNN) that operates directly on the interaction graph and learns a bond-ranking rule mirroring the SDRG decimation policy. The GNN achieves a disorder-averaged pairing accuracy of r P ≃ 0.94 and reproduces the entanglement entropy S ( ℓ ) in excellent quantitative agreement with SDRG across all subsystem sizes and interaction exponents. RG-flow heatmaps confirm that the GNN learns the sequential decimation hierarchy rather than merely fitting final-state observables. Finite-temperature entanglement properties are incorporated via the SDRG-X framework through a two-stage strategy-using the zero-temperature GNN to generate the RG flow and sampling thermal occupations from the canonical ensemble-yielding results in agreement with both numerical SDRG-X and analytical predictions without retraining.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae88b7

Authors: J Vahedi, A Ustyuzhanin, S Kettemann

Institutions: Constructor University, National University of Singapore

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funders: National Research Foundation Singapore, Ministry of Education - Singapore