论文标题
学习利用相关的辅助噪声：可能的量子优势
Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage
论文作者
论文摘要
本文有两条消息。首先，我们证明了处理嘈杂数据的神经网络可以学会利用与数据上噪声相关的辅助噪声的访问。实际上，该网络学会使用相关的辅助噪声作为破译其嘈杂输入数据的近似键。其次，我们表明，对于此任务，噪声增加的缩放行为是使未来的量子机可以具有优势。特别是，分解会在环境中产生相关的辅助噪声。因此，新方法可以通过提供机器学习的量子误差校正来帮助实现未来的量子机。
This paper has two messages. First, we demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. Second, we show that, for this task, the scaling behavior with increasing noise is such that future quantum machines could possess an advantage. In particular, decoherence generates correlated auxiliary noise in the environment. The new approach could, therefore, help enable future quantum machines by providing machine-learned quantum error correction.
