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Universal quantum state preparation via revised greedy algorithm
Quantum Science and Technology ( IF 6.7 ) Pub Date : 2021-09-07 , DOI: 10.1088/2058-9565/ac1dfe
Run-Hong He , Hai-Da Liu , Sheng-Bin Wang , Jing Wu , Shen-Shuang Nie , Zhao-Ming Wang

Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum state. However, the standard greedy (SG) algorithm, in general, cannot take the global maxima and instead becomes stuck on a local maxima. Based on the SG algorithm, in this paper we propose a revised version to design dynamic pulses to realize universal quantum state preparation, i.e. preparing an arbitrary state from another arbitrary one. As applications, we implement this scheme to the universal preparation of single- and two-qubit state in the context of semiconductor quantum dots and superconducting circuits. Evaluation results show that our scheme outperforms the alternative numerical optimizations with higher preparation quality while possesses the comparable high efficiency. Compared with the emerging machine learning, it shows better accessibility and does not require any training. Moreover, the numerical results show that the pulse sequences generated by our scheme are robust against various errors and noises. Our scheme opens a new avenue of optimization in few-level system and limited action space quantum control problems.



中文翻译:

通过修正的贪婪算法制备通用量子态

量子态的制备是量子信息处理的核心。贪心算法提供了一种有效准备量子态的潜在方法。然而,标准贪婪 (SG) 算法通常无法取全局最大值,而是卡在局部最大值上。在SG算法的基础上,我们提出了一个修改版本来设计动态脉冲来实现通用量子态准备,即从另一个任意状态准备一个任意状态。作为应用,我们在半导体量子点和超导电路的背景下将该方案实施到单量子位态和双量子位态的通用制备中。评估结果表明,我们的方案以更高的制备质量优于替代的数值优化,同时具有可比的高效率。与新兴的机器学习相比,它显示出更好的可访问性,并且不需要任何培训。此外,数值结果表明,我们的方案生成的脉冲序列对各种错误和噪声具有鲁棒性。我们的方案为少能级系统和有限动作空间量子控制问题开辟了一条新的优化途径。

更新日期:2021-09-07
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