标题： 基于量子密钥分发协议的机器学习辅助噪声分类 显示英文标题

标题： Machine Learning assisted noise classification with Quantum Key Distribution protocols

摘要： 我们提出了一种混合协议，利用有监督的经典机器学习模型和简单的量子密钥分发协议来分类量子噪声。 我们考虑了在不同噪声条件下量子密钥分发方案中生成的量子比特误码率（QBER），并以高精度识别训练数据和测试数据中的噪声信道。 我们的协议在两种不同场景假设下以高精度分类量子噪声；在第一种情况下，我们假设有两个远程位置的参与方通过噪声量子信道共享密钥，而在第二种情况下，我们在基于门的量子计算机上模拟量子密钥分发协议，其中的门受到噪声影响。 除了高效的分类外，我们的工作还揭示了这两种场景下生成的QBER分布特性的差异。 最后，我们的方法基于从非常简单的量子协议生成的数据的经典后处理，使其在当前低量子比特数的噪声量子计算时代易于实现。 显示英文摘要

摘要： We propose a hybrid protocol to classify quantum noises using supervised classical machine learning models and simple quantum key distribution protocols. We consider the quantum bit error rates (QBERs) generated in QKD schemes under consideration of different noises, and identify the noise channels with high accuracy for both training and test data. Our protocol classifies quantum noises with high accuracy under the assumption of two different scenarios; in one case we assume two remotely located parties share keys through noisy quantum channels, whereas, in the second case, we simulate the QKD protocols on a gate-based quantum computer, where the gates are afflicted with noise. Alongside efficient classification, our work also throws light on the difference in distribution characteristics of QBERs generated in these two scenarios. Finally, our method is based on classical post processing of data generated from very simplistic quantum protocols, making it readily implementable in the current era of noisy quantum computing with low number of qubits.