Quantum generative adversarial learning in a superconducting quantum circuit

TL;DR

This paper presents the first experimental demonstration of quantum generative adversarial learning using a superconducting quantum circuit, achieving high fidelity in replicating quantum data and paving the way for quantum advantages in machine learning.

Contribution

It provides the first proof-of-principle experimental implementation of quantum GANs in a superconducting circuit, validating theoretical predictions.

Findings

Achieved 98.8% fidelity in quantum data replication

Demonstrated training of a quantum state generator via adversarial learning

Showed potential for quantum advantages in machine learning with noisy devices

Abstract

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 splendid performance in a variety of challenging tasks such as image and video generations. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to possess the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum state generator can be trained to replicate the statistics of the quantum data output from a digital qubit channel simulator, with a high fidelity ($98.8\%$ on average) that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate-scale quantum devices.