/jumera

Library/module with Julia code to learn an efficient representation of MERA tensor networks. For modeling many-body quantum states, given a microscopic Hamiltonian.

Primary LanguageJupyter Notebook

This project aims to 'learn' an optimized 'Multiscale Entanglement Renormalization Ansatz' (MERA) representation for the ground state of a many-body quantum system, given a microscopic Hamiltonian. The MERA is most powerful when solving for scale invariant states, such as the ground state of the critical Ising model. The MERA allows us to numerically obtain conformal data, and efficiently calculate correlation functions, for the state it represents. In its current avatar, the code finds the optimal tensors by means of energy minimization.

The references below explain the physics behind MERA, and the algorithmic aspects involved.

The code in this repository has been written/checked to work with Julia v0.5.0.

References

  1. Vidal Entanglement renormalization
  2. Vidal A class of quantum many-body states that can be efficiently simulated
  3. Pfeifer, Evenbly, Vidal Entanglement renormalization, scale invariance and quantum criticality
  4. Evenbly, Vidal Algorithms for entanglement renormalization