Heat-bath algorithmic cooling (AC) of spins is a theoretically powerful
effective cooling approach, that (ideally) cools spins with low polarization
exponentially better than cooling by reversible entropy manipulations alone.
Here, we investigate the limitations and prospects of AC. For non-ideal and
semioptimal AC, we study the impact of finite relaxation times of reset and
computation spins on the achievable effective cooling. We derive, via
simulations, the attainable cooling levels for given ratios of relaxation times
using two semioptimal practicable algorithms. We expect this analysis to be
valuable for the planning of future experiments. For ideal and optimal AC, we
make use of lower bounds on the number of required reset steps, based on
entropy considerations, to present important consequences of using AC as a tool
for improving signal-to-noise ratio in liquid-state magnetic resonance
spectroscopy. We discuss the potential use of AC for noninvasive clinical
diagnosis and drug monitoring, where it may have significantly lower specific
absorption rate (SAR) with respect to currently used methods.Comment: 12 pages, 5 figure