  The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial
opportunities for the development of quantum algorithms. Here we evaluate the
noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ
devices, namely, dissipative QNN (DQNN) whose building-block perceptron is a
completely positive map, and the quantum approximate optimization algorithm
(QAOA). We compare these two approaches to learning an unknown unitary. While
both networks succeed in this learning task, we find that a DQNN learns an
unknown unitary more reliably than QAOA and is less susceptible to gate noise.
