Paramagnetic materials and practical algorithmic cooling for NMR quantum computing

Algorithmic cooling is a method that uses novel data compression techniques and simple quantum computing devices to improve NMR spectroscopy, and to offer scalable NMR quantum computers. The algorithm recursively employs two steps. A reversible entropy compression of computation quantum-bits (qubits) of the system and an irreversible heat transfer from the system to the environment through a set of reset qubits that reach thermal relaxation rapidly. Is it possible to experimentally demonstrate algorithmic cooling using existing technology? To allow experimental algorithmic cooling, the thermalization time of the reset qubits must be much shorter than the thermalization time of the computation qubits. However, such high thermalization-times ratios have yet to be reported. We investigate here the effect of a paramagnetic salt on the thermalization-times ratio of computation qubits (carbons) and a reset qubit (hydrogen). We show that the thermalization-times ratio is improved by approximately three-fold. Based on this result, an experimental demonstration of algorithmic cooling by thermalization and magnetic ions has been performed by the authors and collaborators.