Abstract Designing robust control schemes in n-level open quantum system is significant for quantum computation. Here, we investigate two quantum control strategies based on supervised machine learning to suppress the quantum noise in an open quantum system. One is controlling state distance and the other is governing the average of a Hermitian operator. In this process, the dynamics of the system is mapped to a neural network where the control fields correspond to the weights. Besides, the system is transformed into the coherence Bloch space without using superoperator thus the complications are reduced largely. As an example, the two control protocols are demonstrated in a two-level and four-level systems, respectively. By applying these examples, the results show that the state of the system transfers to the target state and the average of a Hermitian operator to its minimum value in a given time despite disturbed by various types of noise.

中文翻译：

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开放量子系统中基于机器学习的量子控制

摘要 在 n 级开放量子系统中设计鲁棒控制方案对于量子计算具有重要意义。在这里，我们研究了两种基于监督机器学习的量子控制策略，以抑制开放量子系统中的量子噪声。一个是控制状态距离，另一个是控制 Hermitian 算子的平均值。在这个过程中，系统的动力学被映射到一个神经网络，其中控制字段对应于权重。此外，该系统在不使用超级算子的情况下被转化为相干布洛赫空间，从而大大降低了复杂性。例如，这两种控制协议分别在两级和四级系统中进行演示。通过应用这些例子，