Transfer learning in hybrid classical-quantum neural networks
This work addresses the challenge of efficiently integrating quantum elements into machine learning for tasks like image processing in the era of intermediate-scale quantum technology, though it appears incremental as it extends existing transfer learning concepts to a hybrid context.
The authors tackled the problem of applying transfer learning to hybrid classical-quantum neural networks, focusing on augmenting pre-trained classical networks with variational quantum circuits, and demonstrated proof-of-concept examples for image recognition and quantum state classification using quantum computers from IBM and Rigetti.
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.