dorsal/arxiv
View SchemaAlgorithmic Cooling of Spins: A Practicable Method for Increasing Polarization
| Authors | Jose M. Fernandez, Seth Lloyd, Tal Mor, Vwani Roychowdhury |
|---|---|
| Categories | |
| ArXiv ID | quant-ph/0401135 |
| URL | https://arxiv.org/abs/quant-ph/0401135 |
| Journal | International Journal of Quantum Information, v2, n4, pp 461-467, 2004 |
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 spin-cooling techniques are highly limited in their efficiency and usefulness. Algorithmic cooling is a promising new spin-cooling 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. This practicable algorithmic cooling could lead to breakthroughs in high-sensitivity NMR spectroscopy in the near future, and to the development of scalable NMR quantum computers in the far future. Moreover, while the cooling algorithm itself is classical, it uses quantum gates in its implementation, thus representing the first short-term application of quantum computing devices.
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"abstract": "An efficient technique to generate ensembles of spins that are highly\npolarized by external magnetic fields is the Holy Grail in Nuclear Magnetic\nResonance (NMR) spectroscopy. Since spin-half nuclei have steady-state\npolarization biases that increase inversely with temperature, spins exhibiting\nhigh polarization biases are considered cool, even when their environment is\nwarm. Existing spin-cooling techniques are highly limited in their efficiency\nand usefulness. Algorithmic cooling is a promising new spin-cooling approach\nthat employs data compression methods in open systems. It reduces the entropy\nof spins on long molecules to a point far beyond Shannon\u0027s bound on reversible\nentropy manipulations (an information-theoretic version of the 2nd Law of\nThermodynamics), thus increasing their polarization. Here we present an\nefficient and experimentally feasible algorithmic cooling technique that cools\nspins to very low temperatures even on short molecules. This practicable\nalgorithmic cooling could lead to breakthroughs in high-sensitivity NMR\nspectroscopy in the near future, and to the development of scalable NMR quantum\ncomputers in the far future. Moreover, while the cooling algorithm itself is\nclassical, it uses quantum gates in its implementation, thus representing the\nfirst short-term application of quantum computing devices.",
"arxiv_id": "quant-ph/0401135",
"authors": [
"Jose M. Fernandez",
"Seth Lloyd",
"Tal Mor",
"Vwani Roychowdhury"
],
"categories": [
"quant-ph"
],
"journal_ref": "International Journal of Quantum Information, v2, n4, pp 461-467,\n 2004",
"title": "Algorithmic Cooling of Spins: A Practicable Method for Increasing Polarization",
"url": "https://arxiv.org/abs/quant-ph/0401135"
},
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