Prospects and Limitations of Algorithmic Cooling

TL;DR

This paper analyzes the potential and limitations of heat-bath algorithmic cooling (AC) for spins, highlighting how finite relaxation times affect cooling efficiency and exploring applications in magnetic resonance spectroscopy and clinical diagnostics.

Contribution

It provides a detailed analysis of non-ideal and semioptimal AC considering relaxation times, and establishes bounds on reset steps for ideal AC, informing future experimental and practical applications.

Findings

Finite relaxation times limit achievable cooling levels.

Lower bounds on reset steps are derived from entropy considerations.

AC can improve signal-to-noise ratio in magnetic resonance spectroscopy.

Abstract

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.