Quantum generative adversarial learning for simultaneous multiparameter estimation

TL;DR

This paper demonstrates an experimental quantum generative adversarial learning method that uses adaptive feedback and stochastic gradient descent to improve quantum parameter estimation and characterization, even under noisy conditions.

Contribution

It presents the first experimental implementation of quantum generative adversarial learning with adaptive feedback for multiparameter quantum estimation.

Findings

Effective quantum multiparameter estimation demonstrated

Quantum GAN shows advantages under noisy conditions

Adaptive feedback enhances learning performance

Abstract

Generative adversarial learning is currently one of the most prolific fields in artificial intelligence due to its great performance in a variety of challenging tasks such as photorealistic image and video generation. While a quantum version of generative adversarial learning has emerged that promises exponential advantages over its classical counterpart, its experimental implementation and potential applications with accessible quantum technologies remain explored little. Here, we report an experimental demonstration of quantum generative adversarial learning with the assistance of adaptive feedback that is based on stochastic gradient descent algorithm. Its performance is explored by applying this technique to the adaptive characterization of quantum dynamics and simultaneous estimation of multiple phases. These results indicate the intriguing advantages of quantum generative adversarial learning even in the presence of deleterious noise, and pave the way towards quantum-enhanced information processing applications.