Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2021
- Source ID
- 10.1002/qute.202000069
Entities
People
- Abhinav Anand
- Alán Aspuru-Guzik
- Jonathan Romero
- Matthias Degroote
Organizations
- Canada Excellence Research Chairs
- Canadian Institute for Advanced Research
- Lawrence Berkeley National Laboratory
- Office of Naval Research
- University of Toronto
- Vector Institute
- Zapata Computing, Inc.