前馈神经网络识别量子上下文的不确定性

Jan Wasilewski, Tomasz Paterek, Karol Horodecki
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引用次数: 0

摘要

描述神经网络应用于量子领域问题的性能的通常指标是它们的准确性,即在先前未见过的输入上得到正确答案的概率。在这里,我们将该参数与预测的不确定性附加在一起,以表征对答案的置信度。贝叶斯神经网络(BNNs)为估计不确定性提供了一种强大的技术。我们首先给出了简单的例子来说明bnn带来的优势,从中我们希望强调它们即使在使用有偏差的数据集训练后也能进行可靠的不确定性估计的能力。然后将bnn应用于量子上下文识别问题,结果表明不确定性本身是识别上下文错误分类几率的独立参数。
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Uncertainty of feed forward neural networks recognizing quantum contextuality
Abstract The usual figure of merit characterizing the performance of neural networks applied to problems in the quantum domain is their accuracy, being the probability of a correct answer on a previously unseen input. Here we append this parameter with the uncertainty of the prediction, characterizing the degree of confidence in the answer. A powerful technique for estimating uncertainty is provided by Bayesian neural networks (BNNs). We first give simple illustrative examples of advantages brought forward by BNNs, out of which we wish to highlight their ability of reliable uncertainty estimation even after training with biased datasets. Then we apply BNNs to the problem of recognition of quantum contextuality, which shows that the uncertainty itself is an independent parameter identifying the chance of misclassification of contextuality.
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