Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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END-nSDE reconstructs SDEs from heterogeneous cell trajectories via Wasserstein distance, applied to circadian rhythms, RPA-DNA binding, and NFκB signaling while outperforming RNNs and LSTMs.
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations
END-nSDE reconstructs SDEs from heterogeneous cell trajectories via Wasserstein distance, applied to circadian rhythms, RPA-DNA binding, and NFκB signaling while outperforming RNNs and LSTMs.