量子信息科学中以数据为中心的机器学习
Data-Centric Machine Learning in Quantum Information Science
摘要：
我们提出了一系列以数据为中心的算法，用于提高机器学习系统在应用于量子信息科学问题时的性能。特别是，我们考虑如何训练集的系统工程可以显着提高用于量子态重建的预训练神经网络的准确性，而不改变底层架构。我们发现，将训练集设计为与目标场景的预期分布完全匹配并不总是最佳的，相反，可以通过将训练集偏置为比目标稍微更混合来进一步提高性能。这是由于描述不同纯度的状态所需的自由变量数量的异质性，因此，当固定大小的训练集集中于具有最少约束的自由变量的状态时，网络的整体准确性提高。为了进一步清楚，我们还包括一个“玩具模型”演示虚假相关如何无意中进入用于训练的合成数据集，使用这些相关性训练的系统的性能如何显着降低，以及如何包含相对较少的反例可以有效地解决这些问题。
英文摘要：
We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a "toy model" demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.
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