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Spin qubits in semiconductor quantum dots are a promising platform forquantum computing, however scaling to large systems is hampered by crosstalkand charge noise. Crosstalk here refers to the unwanted off-resonant rotationof idle qubits during the resonant rotation of the target qubit. For athree-qubit system with crosstalk and charge noise, it is difficult toanalytically create gate protocols that produce three-qubit gates, such as theToffoli gate, directly in a single shot instead of through the composition oftwo-qubit gates. Therefore, we numerically optimize a physics-informed neuralnetwork to produce theoretically robust shaped pulses that generate aToffoli-equivalent gate. Additionally, robust $\frac{\pi}{2}$ $X$ and CZ gatesare also presented in this work to create a universal set of gates robustagainst charge noise. The robust pulses maintain an infidelity of $10^{-3}$ foraverage quasistatic fluctuations in the voltage of up to a few mV instead oftenths of mV for non-robust pulses.

Neural-network-designed three-qubit gates robust against charge noise and crosstalk in silicon