arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.03018quant-phq-bio.NC
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函数逼近、二维数据分类和序列分类的量子随机游走的实现

Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification

Lu-Ji Wang, Jia-Yi Lin, Shengjun Wu

摘要:

研究了一种基于图上量子随机游动的量子随机神经网络(QSNN),并利用梯度下降法更新网络参数。我们应用玩具模型的QSNN与几个神经元的函数逼近,二维数据分类和序列分类的问题。一个简单的五个神经元的QSNN被训练来确定一个单词序列是否是一个句子,我们发现QSNN可以减少训练步骤的数量。具有11个神经元的QSNN在提高识别新类型输入(如诗句)的准确性方面显示出量子优势。此外,我们的玩具模型,我们发现相干QSNN是更强大的标签噪声和设备噪声相比,退相干QSNN。这些结果表明,量子随机游走可能是实现量子神经网络的有用资源。

英文摘要:

We study a quantum stochastic neural network (QSNN) based on quantum stochastic walks on a graph, and use gradient descent to update the network parameters. We apply a toy model of QSNN with a few neurons to the problems of function approximation, two-dimensional data classification, and sequence classification. A simple QSNN with five neurons is trained to determine whether a sequence of words is a sentence or not, and we find that a QSNN can reduce the number of training steps. A QSNN with 11 neurons shows a quantum advantage in improving the accuracy of recognizing new types of inputs like verses. Moreover, with our toy model, we find the coherent QSNN is more robust against both label noise and device noise, compared with the decoherent QSNN. These results show that quantum stochastic walks may be a useful resource to implement a quantum neural network.

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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