  We extend the concept of transfer learning, widely applied in modern machine
learning algorithms, to the emerging context of hybrid neural networks composed
of classical and quantum elements. We propose different implementations of
hybrid transfer learning, but we focus mainly on the paradigm in which a
pre-trained classical network is modified and augmented by a final variational
quantum circuit. This approach is particularly attractive in the current era of
intermediate-scale quantum technology since it allows to optimally pre-process
high dimensional data (e.g., images) with any state-of-the-art classical
network and to embed a select set of highly informative features into a quantum
processor. We present several proof-of-concept examples of the convenient
application of quantum transfer learning for image recognition and quantum
state classification. We use the cross-platform software library PennyLane to
experimentally test a high-resolution image classifier with two different
quantum computers, respectively provided by IBM and Rigetti.
