Algorithmic Cooling in Liquid State NMR

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.

6 pages, 7 figures

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• Thermodynamics of a minimal algorithmic cooling refrigerator
• Controlling NMR spin systems for quantum computation
• State-independent robust heat-bath algorithmic cooling of nuclear spins
• Transient effects in quantum refrigerators with finite environments