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arXiv 2102.02369quant-ph
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基于机器学习的量子态保真度直接估计

Direct Fidelity Estimation of Quantum States using Machine Learning

Xiaoqian Zhang, Maolin Luo, Zhaodi Wen, Qin Feng, Shengshi Pang, Weiqi Luo, Xiaoqi Zhou

摘要:

在几乎所有的量子应用中,关键步骤之一是验证所制备的量子态的保真度是否符合预期。在这封信中,我们提出了一种使用机器学习技术解决这个问题的新方法。与其他保真度估计方法相比,我们的方法适用于任意量子态,所需的测量设置数量很小,并且这个数量不会随着系统的大小而增加。例如,对于一般的五量子比特量子态,仅需要四个测量设置来在非对抗场景中以$\pm1\%$精度预测其保真度。这种基于机器学习的量子态保真度估计方法在量子信息领域具有广泛的应用前景。

英文摘要:

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $\pm1\%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

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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