论文标题
基于机器学习的歧视激发状态促进读数
Machine Learning based Discrimination for Excited State Promoted Readout
论文作者
论文摘要
读出超导量子位的读数保真度的一个限制因素是将量子放松到谐振器达到其最终目标状态所需的时间之前。提出了一种称为激发态（ESP）读数的技术，以减少这种效果，并进一步改善超导硬件的读数对比度。在这项工作中，我们使用IBM五量量子系统的读数数据来衡量使用深神经网络（例如前馈神经网络）以及各种分类算法（例如K-Neartialt邻居，决策树和高斯幼稚的贝叶斯）进行单品和多Qubit歧视的有效性。将这些方法与标准使用的线性和二次判别分析算法进行了比较，这些算法基于其Qubit-State-State-State-Spation-Spation-nignment Fidelity绩效，对读取串扰的稳健性和训练时间。
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.
