  Quantum computers promise improving machine learning. We investigated the
performance of new quantum neural network designs. Quantum neural networks
currently employed rely on a feature map to encode the input into a quantum
state. This state is then evolved via a parameterized variational circuit.
Finally, a measurement is performed and post-processed on a classical computer
to extract the prediction of the quantum model. We develop a new technique,
where we merge feature map and variational circuit into a single parameterized
circuit and post-process the results using a classical neural network. On a
variety of real and generated datasets, we show that the new, combined approach
outperforms the separated feature map & variational circuit method. We achieve
lower loss, better accuracy, and faster convergence.
