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Related Experiment Video

Updated: Jan 29, 2026

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

Quantum generative adversarial learning in a superconducting quantum circuit.

Ling Hu¹, Shu-Hao Wu², Weizhou Cai¹

¹Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.

Science Advances

|February 13, 2019

Summary

This summary is machine-generated.

Researchers experimentally demonstrated quantum generative adversarial learning using superconducting circuits. This quantum machine learning approach successfully trained a quantum generator to mimic real quantum data with high fidelity.

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Area of Science:

Quantum Computing
Machine Learning
Artificial Intelligence

Background:

Generative adversarial learning (GAL) is a powerful machine learning technique.
Quantum GAL offers potential exponential advantages over classical GAL.
Previous work on quantum GAL was primarily theoretical.

Purpose of the Study:

To experimentally demonstrate quantum generative adversarial learning (QGAL).
To showcase the capabilities of superconducting quantum circuits for QGAL.
To validate the potential of QGAL in machine learning tasks.

Main Methods:

Implemented a proof-of-principle experiment using a superconducting quantum circuit.
Trained a quantum-state generator through adversarial learning.
Utilized a quantum channel simulator to generate target quantum data statistics.

Main Results:

Achieved high-fidelity replication (98.8% average) of quantum data statistics.
Demonstrated that the discriminator could not distinguish generated from true data.
Successfully trained a quantum generator in a proof-of-principle experiment.

Conclusions:

This work provides the first experimental demonstration of QGAL.
Superconducting quantum circuits are viable platforms for QGAL.
Opens avenues for exploring quantum advantages in machine learning on current quantum devices.