论文标题
两倍的CZ门在使用神经网络补偿串扰的同时，在硅的电荷噪声方面有强大的电荷噪声
Two-qubit CZ gates robust against charge noise in silicon while compensating for crosstalk using neural network
论文作者
论文摘要
使用硅自旋矩形的两倍大门的保真度受电荷噪声的限制。当尝试使用局部回声脉冲动态补偿电荷噪声时，串扰会引起并发症。我们提出了一种使用深神经网络来优化分析设计的复合脉冲序列的组件的方法，从而导致双Quibit Gate与电荷噪声误差稳健，同时还考虑了Crosstalk。我们分析了两个实验动机的场景。对于具有强大的EDSR驾驶和可忽略不计的串扰的情况，复合脉冲序列可在简单的余弦脉冲上提高到一个数量级的改进。在具有中等ESR驾驶和明显串扰的情况下，简单的分析控制场无效，使用神经网络方法进行优化，尽管有串扰，但使用神经网络方法可以维持优势的改善。
The fidelity of two-qubit gates using silicon spin qubits is limited by charge noise. When attempting to dynamically compensate for charge noise using local echo pulses, crosstalk can cause complications. We present a method of using a deep neural network to optimize the components of an analytically designed composite pulse sequence, resulting in a two-qubit gate robust against charge noise errors while also taking crosstalk into account. We analyze two experimentally motivated scenarios. For a scenario with strong EDSR driving and negligible crosstalk, the composite pulse sequence yields up to an order of magnitude improvement over a simple cosine pulse. In a scenario with moderate ESR driving and appreciable crosstalk such that simple analytical control fields are not effective, optimization using the neural network approach allows one to maintain order-of-magnitude improvement despite the crosstalk.
