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Quantum generative adversarial networks with multiple superconducting qubits

Kaixuan Huang, Zheng‐An Wang, Chao Song +11 more

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.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.

arXiv:2009.12827npj Quantum InformationDec 1, 202156 citations

Benchmarking superconducting qubits with generative model learning

Kathleen E. Hamilton, Eugen Dumitrescu, Holly G. Stemp +1 more

Bulletin of the American Physical SocietyMar 6, 20190 citations

Adversarially Robust Quantum Transfer Learning

Amena Khatun, Muhammad Usman

arXiv:2510.16301Quantum science and technologyJan 1, 20260 citations

Quantum generative adversarial learning in a superconducting quantum circuit

Ling Hu, Shu-Hao Wu, Weizhou Cai +8 more

A quantum version of generative adversarial learning is experimentally demonstrated with a superconducting circuit.

Bulletin of the American Physical SocietyMar 5, 201964 citations

Quantum generative adversarial learning in a superconducting quantum circuit

Ling Hu, Shu-Hao Wu, Weizhou Cai +8 more

Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have 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 quantum channel simulator, with a high fidelity (98.8% on average) so 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.Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have 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 quantum channel simulator, with a high fidelity (98.8% on average) so 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.

arXiv:1808.02893Science AdvancesJan 4, 2019165 citations

Adversarial Robustness in Quantum Machine Learning

Nicola Franco, Aman Saxena, Tom Wollschläger +3 more

Quantum science and technologyJan 1, 20260 citations

Experimental quantum adversarial learning with programmable superconducting qubits

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