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arXiv 2101.03221quant-phcond-mat.dis-nncs.AIcs.LGcs.NE
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非马尔可夫噪声干扰量子动力学的机器学习分类

Machine learning classification of non-Markovian noise disturbing quantum dynamics

University of Florence(佛罗伦萨大学);European Laboratory for Non-Linear Spectroscopy (LENS)(欧洲非线性光谱实验室(LENS));CNR-INO(意大利国家研究委员会-国家光学研究所)

Stefano Martina, Stefano Gherardini, Filippo Caruso

摘要:

在本文中,机器学习和人工神经网络模型提出的外部噪声源的分类影响一个给定的量子动力学。为此,我们训练,然后验证支持向量机,多层感知器和递归神经网络模型具有不同的复杂性和准确性,解决监督二进制分类问题。因此,我们证明了高效率的工具,在分类噪声量子动力学模拟数据集从不同的实现的量子系统动力学。此外,我们表明,一个成功的分类只需要测量,在一个序列的离散时刻,分析的量子系统是在一个允许的位置或能量配置的概率。尽管机器学习模型的训练是在合成数据上进行的,但我们的方法有望在实验方案中找到应用,例如用于噪声中等规模量子设备的噪声基准测试。

英文摘要:

In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.

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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