Impact of quantum noise on the training of quantum Generative
  Adversarial Networks
Current noisy intermediate-scale quantum devices suffer from various sources
of intrinsic quantum noise. Overcoming the effects of noise is a major
challenge, for which different error mitigation and error correction techniques
have been proposed. In this paper, we conduct a first study of the performance
of quantum Generative Adversarial Networks (qGANs) in the presence of different
types of quantum noise, focusing on a simplified use case in high-energy
physics. In particular, we explore the effects of readout and two-qubit gate
errors on the qGAN training process. Simulating a noisy quantum device
classically with IBM's Qiskit framework, we examine the threshold of error
rates up to which a reliable training is possible. In addition, we investigate
the importance of various hyperparameters for the training process in the
presence of different error rates, and we explore the impact of readout error
mitigation on the results.
Kerstin Borras Su Yeon Chang Lena Funcke Michele Grossi Tobias Hartung Karl Jansen Dirk Kruecker Stefan Kühn Florian Rehm Cenk Tüysüz Sofia Vallecorsa