Algorithmic Cooling in Liquid State NMR

TL;DR

This paper demonstrates the use of optimal control techniques to implement algorithmic cooling in liquid state NMR, achieving significant entropy reduction and advancing quantum computing applications in magnetic resonance spectroscopy.

Contribution

It introduces the application of gradient ascent pulse engineering to enhance algorithmic cooling in liquid state NMR, surpassing Shannon's entropy bound in experiments.

Findings

Achieved a cooling factor of 4.61 on a carbon qubit.

Successfully cooled the system beyond Shannon's entropy limit.

Demonstrated potential for integrating NMR quantum computing with in vivo spectroscopy.

Abstract

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 $^{13}$C$_2$-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.