88 research outputs found

    Quantum generative adversarial learning

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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. 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 consists of samples of measurements made on high-dimensional spaces, quantum adversarial networks may exhibit an exponential advantage over classical adversarial networks.Comment: 5 pages, 1 figur

    Continuous-variable dense coding by optomechanical cavities

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    In this paper, we show how continuous-variable dense coding can be implemented using entangled light generated from a membrane-in-the-middle geometry. The mechanical resonator is assumed to be a high reflectivity membrane hung inside a high quality factor cavity. We show that the mechanical resonator is able to generate an amount of entanglement between the optical modes at the output of the cavity, which is strong enough to approach the capacity of quantum dense coding at small photon numbers. The suboptimal rate reachable by our optomechanical protocol is high enough to outperform the classical capacity of the noiseless quantum channel
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