<ul><li>The integration of quantum computing (QC) and machine learning (ML) has given rise to quantum machine learning (QML), with variational quantum circuits (VQCs) showing promise in this field.</li><li>The Quantum-Train (QT) framework addresses the challenges faced by VQCs by generating classical neural network parameters for inference without the need for quantum hardware, enabling significant parameter compression.</li><li>An automated solution utilizing differentiable optimization is proposed in this paper to design effective quantum circuit architectures for quantum-enhanced neural networks.</li><li>The evaluation of the proposed framework shows that it can outperform manually designed quantum neural network architectures across various tasks, providing a scalable and automated approach for designing QNNs.</li></ul>

Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation

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