Extracting average properties of disordered spin chains with translationally invariant tensor networks
Abstract
We develop a tensor network-based method for calculating disorder-averaged finite-temperature expectation values in random spin chains without having to explicitly sample over disorder configurations. The algorithm exploits statistical translation invariance and works directly in the thermodynamic limit. We benchmark our method on the infinite-randomness critical point of the random transverse field Ising model.
Fields of Study & Subjects
Quantum many-body systems
Theoretical and Computational Physics
Quantum Computing Algorithms and Architecture
Publication details
- Journal / source
- SciPost Physics Core
- Volume
- 9
- Issue
- 2
- Publication date
- 29 June 2026
- Language
- EN