Qubit arrays with access to high-fidelity single-and two-qubit gates are a key ingredient for building a quantum computer. As semiconductor-based devices with several qubits become available, issues like residual interqubit coupling and additional constraints from scalable control hardware need to be tackled to retain the high gate fidelities demonstrated in single-qubit devices. Focusing on two exchange-coupled singlet-triplet spin qubits, we address these issues by considering realistic control hardware as well as Coulomb and interqubit exchange coupling that cannot be fully turned off. We use measured noise spectra for charge and magnetic field noise to numerically optimize experimentally realistic pulse sequences and show that two-qubit (single-qubit) gate fidelities of 99.90% (≥ 99.69%) can be reached in GaAs, while 99.99% (≥ 99.95%) can be achieved with vanishing magnetic field noise as in Si.

While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots, and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.