Quantum generative adversarial networks

By Pierre-Luc Dallaire-Demers, Nathan Killoran. Physical Review A, 2018

508 citations recorded by the source corpus.

Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Furthermore, we also show how to compute gradients -- a key element in generative adversarial network training -- using another quantum circuit. We give an example of a simple practical circuit ansatz to parametrize quantum machine learning models and perform a simple numerical experiment to demonstrate that quantum generative adversarial networks can be trained successfully.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/10eec18a20954f9ac4d8f463524e5f160e23895f

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Citations in the corpus

Quantum Generative Adversarial Learning. (2018, background): “Note that a more in-depth practical implementation of QuGANs can be found in a companion paper [15].”
Quantum Generative Adversarial Networks for learning and loading random distributions (2019, background): “finding a quantum channel whose output is an estimate to a given quantum state [14–16].”
Challenges and opportunities in quantum machine learning (2022, unknown): “[42] Pierre-Luc Dallaire-Demers and Nathan Killoran,”
Robust data encodings for quantum classifiers (2020, background): “Such applications include generative modeling [27–31], transfer learning [32], and classiﬁcation [25, 33–38].”
Continuous-variable quantum neural networks (2018, unknown): “[84] Pierre-Luc Dallaire-Demers and Nathan Killoran.”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, background): “…more complex machine learing tasks such as training a hybrid quantum-classical machine learning model in a supervised fashion, or training a generative adverserial network, both when discriminator and generator are quantum models [ 14 ] and when one is quantum and the other is classical [ 34 ] .”