Transfer learning in hybrid classical-quantum neural networks

By Andrea Mari, Thomas R. Bromley, Josh A. Izaac, Maria Schuld, Nathan Killoran. Quantum, 2019

445 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d1a4e0e8ef393cb343d7a407f42b4aa091e5cb1f

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Citations in the corpus

General parameter-shift rules for quantum gradients (2021, background): “…as optimization [8], quantum chemistry [9, 10, 11, 12, 13], integer factorization [14], compilation [15], quantum control [16], matrix diagonaliza-David Wierichs: wierichs@thp.uni-koeln.de tion [17, 18], and variational quantum machine learning [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31].”
Robust data encodings for quantum classifiers (2020, background): “Such applications include generative modeling [27–31], transfer learning [32], and classiﬁcation [25, 33–38].”