Algorithmic cooling is a method that employs thermalization to increase qubit purification level, namely it reduces the qubit-system's entropy. We utilized gradient ascent pulse engineering (GRAPE), an optimal control algorithm, to implement algorithmic cooling in liquid state nuclear magnetic resonance. Various cooling algorithms were applied onto the three qubits of 13C2-trichloroethylene, cooling the system beyond Shannon's entropy bound in several different ways. In particular, in one experiment a carbon qubit was cooled by a factor of 4.61. This work is a step towards potentially integrating tools of NMR quantum computing into in vivo magnetic resonance spectroscopy.
@article{arxiv.1411.4641,
title = {Algorithmic Cooling in Liquid State NMR},
author = {Yosi Atia and Yuval Elias and Tal Mor and Yossi Weinstein},
journal= {arXiv preprint arXiv:1411.4641},
year = {2016}
}