A limiting factor for readout fidelity for superconducting qubits is the relaxation of the qubit to the ground state before the time needed for the resonator to reach its final target state. A technique known as excited state promoted (ESP) readout was proposed to reduce this effect and further improve the readout contrast on superconducting hardware. In this work, we use readout data from IBM's five-qubit quantum systems to measure the effectiveness of using deep neural networks, like feedforward neural networks, and various classification algorithms, like k-nearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These methods were compared to standardly used linear and quadratic discriminant analysis algorithms based on their qubit-state-assignment fidelity performance, robustness to readout crosstalk, and training time.

翻译：超导二次比特的读出忠贞度的一个限制因素是,在共振器达到最终目标状态之前,正方位放松到地面状态。 一种被称为刺激状态(ESP)的读出技术被建议降低这一效应,并进一步改进超导硬件的读出对比。 在这项工作中,我们使用IBM五位位数系统中的读出数据来衡量使用深神经网络(如饲料前神经网络)和各种分类算法(如K近邻、决策树和高山天天敌贝亚斯)的有效性,以进行单位和多位歧视。 这些方法与基于其量位分配忠贞的性表现、阅读交叉对话的稳健性以及培训时间的标准使用的线性与四位相异谱分析算法相比。