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Found 4 papers, 1 papers with code
Transition role of entangled data in quantum machine learning
Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold.
Symmetric Pruning in Quantum Neural Networks
To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima.
The dilemma of quantum neural networks
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliability and interpretability.
Towards understanding the power of quantum kernels in the NISQ era
In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered.
