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Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields.
Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
Taking the ground states of quantum lattice models and random matrix product states as examples, with the number of qubits where processing the full coefficients is unlikely, ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$.
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