【2h】

Algorithmic cooling and scalable NMR quantum computers

机译:算法冷却和可扩展的NMR量子计算机

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摘要

We present here algorithmic cooling (via polarization heat bath)—a powerful method for obtaining a large number of highly polarized spins in liquid nuclear-spin systems at finite temperature. Given that spin-half states represent (quantum) bits, algorithmic cooling cleans dirty bits beyond the Shannon's bound on data compression, by using a set of rapidly thermal-relaxing bits. Such auxiliary bits could be implemented by using spins that rapidly get into thermal equilibrium with the environment, e.g., electron spins. Interestingly, the interaction with the environment, usually a most undesired interaction, is used here to our benefit, allowing a cooling mechanism. Cooling spins to a very low temperature without cooling the environment could lead to a breakthrough in NMR experiments, and our “spin-refrigerating” method suggests that this is possible. The scaling of NMR ensemble computers is currently one of the main obstacles to building larger-scale quantum computing devices, and our spin-refrigerating method suggests that this problem can be resolved.
机译:我们在此介绍算法冷却(通过极化热浴),这是一种在有限温度下获得液态核自旋系统中大量高极化自旋的有效方法。假设自旋半状态代表(量子)位,则算法冷却通过使用一组快速热松弛位来清除超出Shannon数据压缩范围的脏位。可以通过使用迅速与环境达到热平衡的自旋来实现这种辅助位,例如电子自旋。有趣的是,这里与环境的交互(通常是最不希望的交互)在我们的利益中得到了利用,从而实现了冷却机制。在不冷却环境的情况下将自旋冷却至非常低的温度可能会导致NMR实验的突破,而我们的“自旋制冷”方法表明这是可能的。 NMR集成计算机的规模化是当前构建大型量子计算设备的主要障碍之一,我们的自旋制冷方法表明可以解决此问题。

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