  Composite pulses are essential for universal manipulation of singlet-triplet
spin qubits. In the absence of noise, they are required to perform arbitrary
single-qubit operations due to the special control constraint of a
singlet-triplet qubits; while in a noisy environment, more complicated
sequences have been developed to dynamically correct the error. Tailoring these
sequences typically requires numerically solving a set of nonlinear equations.
Here we demonstrate that these pulse sequences can be generated by a
well-trained, double-layer neural network. For sequences designed for the
noise-free case, the trained neural network is capable of producing almost
exactly the same pulses known in the literature. For more complicated
noise-correcting sequences, the neural network produces pulses with slightly
different line-shapes, but the robustness against noises remains comparable.
These results indicate that the neural network can be a judicious and powerful
alternative to existing techniques, in developing pulse sequences for universal
fault-tolerant quantum computation.
