Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions. Issue 5 (1st April 2021) - Anand, Abhinav; Romero, Jonathan; Degroote, Matthias; Aspuru‐Guzik, Alán
Abstract: The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen‐4‐2Q‐A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices. Abstract : Parameterized quantum circuits (PQCs) are central to different machine learning models. In this work a quantum generative adversarial network, with PQCs as generator and a classical neural network as discriminator, is used to generate continuous distribution. The training has been carried out on both simulated and physical quantum devices, and the results indicate that noisy intermediate scale quantum devices can be used for generative learning.