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

Abstract

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 π/2 X and cz 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.