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Algorithmic Cooling of Spins: A Practicable Method for Increasing Polarization

By José M. Fern, Seth Lloyd, Tal Mor and Vwani Roychowdhury

Abstract

An efficient technique to generate ensembles of spins that are highly polarized by external magnetic fields is the Holy Grail in Nuclear Magnetic Resonance (NMR) spectroscopy. Since spin-half nuclei have steady-state polarization biases that increase inversely with temperature, spins exhibiting high polarization biases are considered cool, even when their environment is warm. Existing spincooling techniques [1, 2, 3, 4] are highly limited in their efficiency and usefulness. Algorithmic cooling [5] is a promising new spincooling approach that employs data compression methods in open systems. It reduces the entropy of spins on long molecules to a point far beyond Shannon’s bound on reversible entropy manipulations (an information-theoretic version of the 2nd Law of Thermodynamics), thus increasing their polarization. Here we present an efficient and experimentally feasible algorithmic cooling technique that cools spins to very low temperatures even on short molecules

Year: 2004
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.102
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