Quantum Generative Adversarial Learning.

By Seth Lloyd, Christian Weedbrook. Physical Review Letters, 2018

589 citations recorded by the source corpus.

Generative adversarial networks represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake data. The learning process for generator and discriminator can be thought of as an adversarial game, and under reasonable assumptions, the game converges to the point where the generator generates the same statistics as the true data and the discriminator is unable to discriminate between the true and the generated data. This Letter introduces the notion of quantum generative adversarial networks, where the data consist either of quantum states or of classical data, and the generator and discriminator are equipped with quantum information processors. We show that the unique fixed point of the quantum adversarial game also occurs when the generator produces the same statistics as the data. Neither the generator nor the discriminator perform quantum tomography; linear programing drives them to the optimal. Since quantum systems are intrinsically probabilistic, the proof of the quantum case is different from-and simpler than-the classical case. We show that, when the data consist of samples of measurements made on high-dimensional spaces, quantum adversarial networks may exhibit an exponential advantage over classical adversarial networks.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ea22871302da564c1b5c7ee8fc5f0839133198ff

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Citations in the corpus

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 ] .”
Quantum Generative Adversarial Networks for learning and loading random distributions (2019, unknown): “[12] S.”
Generative Adversarial Networks (GANs) (2020, background): “QuGANs achieves Nash equilibrium when G finds the correct statistics while D cannot find difference between true and generated data.”