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arXiv 2107.07321quant-ph
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量子噪声可以增强算法冷却

Quantum noise can enhance algorithmic cooling

Zahra Farahmand, Reyhaneh Aghaei Saem, Sadegh Raeisi

摘要:

热浴制冷技术(Heat-Bath Chromic Cooling Techniques,HBAC)是一种用于纯化量子系统中目标元素的技术。这些方法将熵从目标元素压缩并转移到系统的辅助元素中。在理想的无噪声条件下,研究了制冷机的制冷性能。然而,现实的实现是不完美的,并且出于实际目的,应该考虑噪声。在这里,我们分析现实噪声模型下的热浴制冷技术。令人惊讶的是,我们发现,在某些情况下,噪音可以提高性能,提高热浴制冷技术的冷却极限。我们数值模拟的噪音算法冷却的两个最佳策略,合作伙伴配对,和两种排序算法。我们发现,对于这两个过程,在广义振幅阻尼噪声存在下,过程收敛,渐近纯度可以高于无噪声过程。这为提高纯度开辟了新的途径,超越了热浴算法冷却。

英文摘要:

Heat-Bath Algorithmic Cooling techniques (HBAC) are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of Algorithmic Cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect and for practical purposes, noise should be taken into account. Here we analyze Heat-Bath Algorithmic Cooling techniques under realistic noise models. Surprisingly, we find that noise can in some cases enhance the performance and improve the cooling limit of Heat-Bath Algorithmic Cooling techniques. We numerically simulate the noisy algorithmic cooling for the two optimal strategies, the Partner Pairing, and the Two-sort algorithms. We find that for both of them, in the presence of the generalized amplitude damping noise, the process converges and the asymptotic purity can be higher than the noiseless process. This opens up new avenues for increasing the purity beyond the heat-bath algorithmic cooling.

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