Semi-optimal Practicable Algorithmic Cooling
Yuval Elias, Tal Mor, Yossi Weinstein

TL;DR
This paper introduces semi-optimal practicable algorithmic cooling (SOPAC), which achieves better cooling than practicable methods and is more efficient than exhaustive algorithms, with potential applications in quantum computing.
Contribution
The paper proposes and analyzes SOPAC algorithms that bridge the gap between practicable and optimal cooling methods, improving efficiency and cooling levels.
Findings
SOPAC algorithms outperform PAC in cooling levels.
SOPAC algorithms are more efficient than exhaustive algorithms.
Few spins are needed to achieve high purity for quantum computing.
Abstract
Algorithmic Cooling (AC) of spins applies entropy manipulation algorithms in open spin-systems in order to cool spins far beyond Shannon's entropy bound. AC of nuclear spins was demonstrated experimentally, and may contribute to nuclear magnetic resonance (NMR) spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; Exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semi-optimal practicable AC (SOPAC), wherein few cycles (typically 2-6) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than…
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