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 ...
[1804.09139] Quantum generative adversarial learning Quantum generative adversarial networks [1804.08641] Quantum generative adversarial networks Benedetti, M., Grant, E., Wossnig, L., & Severini, S. [1806.00463] Adversarial quantum circuit learning for pure state approximation Haozhen Situ, Zhimin He...
In classical machine learning, GANs have proven useful for generative modeling. These algorithms employ two competing neural networks - a generator and a discriminator - which are trained alternately. Replacing either the generator, the discriminator, or both with quantum systems translates the framework...
Generative Adversarial Network. First, the generator creates data samples which shall be indistinguishable from the training data. Second, the discriminator tries to differentiate between the generated samples and the training samples. The generator and discriminator are trained alternately. Full size image...
quantum machine learning Quantum computing Machine learning Cricuit quantum electrodynamics architecture(circuit QED) Properties of the system Our measurement setup with a fast real-time feedback control Quantum channel for arbitrary quantum state generation Experimental quantum generative adversarial learning ...
Machine learning represents an important field with broad applications where q... G Xun,Z Zhang,L Duan 被引量: 7发表: 2017年 Differentiable Learning of Quantum Circuit Born Machine However, similar to the leading implicit generative models in deep learning, such as the generative adversarial ...
For the description of the quantum generative adversarial learning method, see [378]. A scheme on the classification with gate-model quantum neural networks can be found in [379]. Show moreView article Preliminaries Mingsheng Ying, in Foundations of Quantum Programming, 2016...
The concept of quantum generative adversarial learning can be traced back to [35]. [35] discourses the operating efficiency of quantum generative adversarial learning in a variety of situations, such as the training data is classical data or quantum data, and whether the discriminator and generator...
Quantum generative adversarial learning. Phys. Rev. Lett. 121, 040502 (2018). Article ADS MathSciNet Google Scholar Zoufal, C., Lucchi, A. & Woerner, S. Quantum generative adversarial networks for learning and loading random distributions. npj Quantum Inf. 5, 103 (2019). Article ADS ...
The problem of quantum generative adversarial learning is studied in51. In generative adversarial networks a generator entity creates statistics for data that mimics those of a valid data set, and a discriminator unit distinguishes between the valid and non-valid data. As a main conclusion of the...