Spin qubits in semiconductor quantum dots are a promising platform for quantum computing, however, scaling to large systems is hampered by crosstalk and charge noise. Crosstalk here refers to the unwanted off-resonant rotation of idle qubits during the resonant rotation of the target qubit. For a three-qubit system with crosstalk and charge noise, it is difficult to analytically create gate protocols that produce three-qubit gates, such as the Toffoli gate, directly in a single shot instead of through the composition of two-qubit gates. Therefore, we numerically optimize a physics-informed neural network to produce theoretically robust shaped pulses that generate a Toffoli-equivalent gate. Additionally, robust X and Controlled-Z gates are also presented in this work to create a universal set of gates robust against charge noise. The robust pulses maintain an infidelity of 10−3 for average quasistatic fluctuations in the voltage of up to a few mV instead of tenths of mV for non-robust pulses.

中文翻译：

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神经网络设计的三量子位门对硅中的电荷噪声和串扰具有鲁棒性

半导体量子点中的自旋量子位是量子计算的一个有前途的平台，然而，扩展到大型系统受到串扰和电荷噪声的阻碍。这里的串扰是指在目标量子位共振旋转期间闲置量子位不必要的非共振旋转。对于具有串扰和电荷噪声的三量子位系统，很难分析地创建直接在单次中而不是通过两个量子位门的组合来产生三量子位门（例如Toffoli门）的门协议。因此，我们对物理信息神经网络进行数值优化，以产生理论上稳健的成形脉冲，从而生成 Toffoli 等效门。此外，这项工作中还介绍了强大的 X 和受控 Z 门，以创建一组针对电荷噪声具有鲁棒性的通用门。对于高达几 mV 的电压平均准静态波动，鲁棒脉冲保持 10−3 的不保真度，而不是非鲁棒脉冲的十分之一 mV。