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Toward Trainability of Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform clas…
Experimental Quantum Generative Adversarial Networks for Image Generation
He-Liang Huang, Yuxuan Du, Ming Gong +18
Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversari…
On the learnability of quantum neural networks
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +2
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme, which remains largely unknown due to the non-convex optim…
Quantum Geometric Machine Learning for Quantum Circuits and Control
Elija Perrier, Christopher Ferrie, Dacheng Tao
The application of machine learning techniques to solve problems in quantum control together with established geometric methods for solving optimisation problems leads naturally to…
Quantum Differentially Private Sparse Regression Learning
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +2
The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differe…
Quantum noise protects quantum classifiers against adversaries
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +2
Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often playe…