We describe a method for reconstructing multi-scale entangled states from a small number of efficiently-implementable measurements and fast post-processing. The method only requires single particle measurements and the total number of measurements is polynomial in the number of particles. Data post-processing for state reconstruction uses standard tools, namely matrix diagonalisation and conjugate gradient method, and scales polynomially with the number of particles. Our method prevents the build-up of errors from both numerical and experimental imperfections.
@article{arxiv.1204.0792,
title = {Practical learning method for multi-scale entangled states},
author = {Olivier Landon-Cardinal and David Poulin},
journal= {arXiv preprint arXiv:1204.0792},
year = {2014}
}