Quantum generative adversarial networks with multiple superconducting qubits

Kaixuan Huang,Dongning Zheng,Kai Xu,Zheng-An Wang,Chao Song,Heng Fan,H Wang,Dong-Ling Deng,Qiujiang Guo,Hekang Li,Zhen Wang,Zhi-Bo Liu,Jian-Guo Tian,Zixuan Song

doi:10.1038/s41534-021-00503-1

Abstract

Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.

Highlights

The interplay between quantum physics and machine learning gives rise to an emergent research frontier of quantum machine learning that has attracted tremendous attention recently[1,2,3,4]
The general framework of quantum generative adversarial networks (QGANs) consists of a generator learning to generate statistics for data mimicking those of a true data set, and a discriminator trying to discriminate generated data from true data[7,12,14,15,16,17,18]
The first controlled rotation gate can be either a controlled X (CNOT) or controlled Z (CZ) gate depending on the single-qubit rotation axis it follows as shown in the lower right box

Summary

Introduction

The interplay between quantum physics and machine learning gives rise to an emergent research frontier of quantum machine learning that has attracted tremendous attention recently[1,2,3,4]. An intriguing example concerns quantum generative adversarial networks (QGANs)[7], where near-term quantum devices have the potential to showcase quantum supremacy[9] with real-life practical applications. Applications of QGANs with potential quantum advantages in generating high-resolution images[10,11], loading classical data[12], and discovering small molecular drugs[13] have been investigated actively at the current stage. The general framework of QGANs consists of a generator learning to generate statistics for data mimicking those of a true data set, and a discriminator trying to discriminate generated data from true data[7,12,14,15,16,17,18]. In a previous QGAN experiment[16], the generator is trained via the adversarial learning process to replicate the statistics of the single-qubit quantum data output from a quantum channel simulator. The involvement of entanglement in QGANs, which is a characterizing feature of quantumness and a vital resource for quantum supremacy, has not yet been achieved during the learning process[3]

Methods

Results

Conclusion