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arXiv 2408.09183quant-ph
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噪声量子计算机生成的对称态的群不变估计

Group-invariant estimation of symmetric states generated by noisy quantum computers

Federico Holik, Marcelo Losada, Giannina Zerr, Lorena Rebón, Diego Tielas

AI总结:

针对噪声量子计算机生成的对称量子态估计问题,提出基于适配任意对称性的量子最大熵等价估计的群不变方法,可减少测量与计算量,经IonQ处理器实验验证有效且节省资源。

摘要:

量子态估计问题是量子技术发展中的关键问题,其中对称量子态的应用在诸多相关场景中具有重要价值。本文研究了重构由量子处理器生成的对称量子态密度矩阵的任务,为此采用了一种与量子最大熵(MaxEnt)估计等价的估计技术,该技术近期已被适配用于具有任意对称性的量子态。通过巧妙利用量子态对称性的先验知识,该方法既减少了需要对系统进行的测量次数,又降低了存储和处理数据所需的计算问题规模,同时还提升了估计器的整体性能。在完成数值模拟后,研究团队在IonQ量子处理器上制备了若干对称态实例,并使用所提出的技术对其进行估计。实验结果与数值模拟吻合良好,表明该方法是一种优良的估计器,能够同时节省实验资源和计算资源。

英文摘要:

The problem of quantum state estimation is crucial in the development of quantum technologies. In particular, the use of symmetric quantum states is useful in many relevant applications. In this work, we analyze the task of reconstructing the density matrices of symmetric quantum states generated by a quantum processor. For this purpose, we take advantage of an estimation technique that results to be equivalent to the quantum Maximum Entropy (MaxEnt) estimation, and which was recently adapted to quantum states with arbitrary symmetries. The smart use of prior knowledge of the quantum state symmetries allows for a reduction in both, the number of measurements that need to be made on the system, and the size of the computational problem to store and process the data, resulting in a better overall performance of the estimator as well. After performing numerical simulations, we implement some examples of symmetric states in IonQ quantum processors, and estimate them using the proposed technique. The results are in a good agreement with numerical simulations, showing that the proposed method is a good estimator that allows to save both, experimental and computational resources.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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