  With the increased focus on quantum circuit learning for near-term
applications on quantum devices, in conjunction with unique challenges
presented by cost function landscapes of parametrized quantum circuits,
strategies for effective training are becoming increasingly important. In order
to ameliorate some of these challenges, we investigate a layerwise learning
strategy for parametrized quantum circuits. The circuit depth is incrementally
grown during optimization, and only subsets of parameters are updated in each
training step. We show that when considering sampling noise, this strategy can
help avoid the problem of barren plateaus of the error surface due to the low
depth of circuits, low number of parameters trained in one step, and larger
magnitude of gradients compared to training the full circuit. These properties
make our algorithm preferable for execution on noisy intermediate-scale quantum
devices. We demonstrate our approach on an image-classification task on
handwritten digits, and show that layerwise learning attains an 8% lower
generalization error on average in comparison to standard learning schemes for
training quantum circuits of the same size. Additionally, the percentage of
runs that reach lower test errors is up to 40% larger compared to training the
full circuit, which is susceptible to creeping onto a plateau during training.
