Quantum adversarial machine learning lies at the intersection of quantum computing and adversarial machine learning. As the attainment of quantum supremacy demonstrates, quantum computers have already outpaced classical computers in certain domains (Arute et al. in Nature 574:505–510, 2019 [ 3 ])....
we present a feasible learning and loading scheme for generic probability distributions based on a generative model. The scheme utilizes a hybrid quantum-classical implementation of a Generative Adversarial Network (GAN)10,11to train
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...
Finally, autoencoders and generative adversarial networks (GANs) have recently been generalized to the quantum setting13,43,79,80. Both employ training data, and hence our generalization bounds provide quantitative guidance for how much training data to employ in these applications. Moreover, our re...
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 ...
Quantum generative adversarial learning in a superconducting quantum circuit Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning---a subfield of artificial intelligence that is currently driving a revolution in many aspects of modern society. It has shown ...
usingLinearAlgebrausingYaousingQuAlgorithmZoo:random_diff_circuit,pair_ring"""Quantum GAN.Reference:Benedetti, M., Grant, E., Wossnig, L., & Severini, S. (2018).Adversarial quantum circuit learning for pure state approximation, 1–14."""structQuGAN{N}target::ArrayReggenerator::AbstractBlock...
In addition, quantum adversarial machine learning has attracted tremendous attention recently [6]. It would be interesting and of practical importance to study the robustness of QCCNNs to adversarial perturbations and develop effective defense strategies. Finally, an experimental demonstration of QCNNs ...
“In the near-term, Quantum Machine Learning (QML) appears to be the application most compatible with the NISQ devices in use today,” Savoie told VentureBeat. “Recent advances in promising quantum machine learning applications include natural language processing andgenerative adversarial networks (GANs...
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...