Title: Machine Learning-aided Optimal Control of a noisy qubit Show Chinese title

Title: 基于机器学习的噪声量子比特最优控制

Abstract: We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian Ornstein-Uhlenbeck noise and achieve low prediction errors even in challenging noise coupling regimes. Using the model as a dynamics emulator, we perform gradient-based optimal control to identify pulse sequences implementing a universal set of single-qubit gates, achieving fidelities above 99% for the lowest considered value of the coupling and remaining above 90% for the highest. Show Chinese abstract

Abstract: 我们将灰盒机器学习框架应用于建模和控制经历马尔可夫和非马尔可夫环境噪声动力学的量子比特。 该方法结合了物理信息方程与基于自注意力机制的轻量级变换器神经网络。 该模型在模拟数据上进行训练，并学习了一个有效的算符，即使在存在记忆效应的情况下也能准确预测可观测量。 我们对非高斯随机电报噪声和高斯奥恩斯坦-乌伦贝克噪声进行了基准测试，在具有挑战性的噪声耦合区域中也实现了低预测误差。 使用该模型作为动力学模拟器，我们进行了基于梯度的最优控制，以确定实现单量子比特通用门集的脉冲序列，在考虑的最低耦合值下实现了超过99%的保真度，并在最高耦合值下仍保持在90%以上。