论文标题
使用自动编码器增强量子读数
Enhancing Qubit Readout with Autoencoders
论文作者
论文摘要
除了需要稳定且精确可控的量子位，量子计算机还利用了良好的读数方案。可以从通过分散耦合的谐振器传输的读数信号来推断超导量子态。这项工作提出了一种新颖的读数分类方法，用于基于通过自动编码器方法预先训练的神经网络超导Qubits。神经网络已通过Qubit读数信号作为自动编码器进行预训练，以便从数据集中提取相关功能。之后，预训练的网络内层值用于以监督方式执行输入的分类。我们证明了这种方法可以提高分类性能，尤其是对于更传统的方法呈现较低性能的短期和长时间测量。
In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. This work proposes a novel readout classification method for superconducting qubits based on a neural network pre-trained with an autoencoder approach. A neural network is pre-trained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterwards, the pre-trained network inner layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short and long time measurements where more traditional methods present lower performance.
