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2018

Quantum generative adversarial learning

Lloyd, Seth, Weedbrook, Christian

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Generative adversarial networks (GANs) 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 paper introduces the notion of quantum generative adversarial networks (QuGANs), where the data consists 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.

Quantum generative adversarial learning · Around

Built on

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