Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.

中文翻译：

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具有层次结构的量子ansatz的机器学习分布

机器学习技术可以帮助表示和解决量子系统。学习量子分析的测量结果分布对于表征近期量子计算设备很有用。在这项工作中，我们使用流行的无监督机器学习模型变分自编码器 (VAE) 来重建量子 ansatz 的测量结果分布。将 VAE 中的参数数量与测量结果的数量进行比较。数值结果表明，VAE 可以通过少量参数有效地学习测量结果分布。还揭示了纠缠对任务的影响。