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A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers.
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model
We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made.
Quantum Neural Network Compression
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices.
Measurement error models: from nonparametric methods to deep neural networks
The success of deep learning has inspired recent interests in applying neural networks in statistical inference.
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