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Approximating Ground States by Neural Network Quantum States
Ying Yang1,2, Chengyang Zhang1, Huaixin Cao1
1School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.
Abstract:
Motivated by the Carleo's work (Science, 2017, 355: 602), we focus on finding the neural network quantum statesapproximation of the unknown ground state of a given Hamiltonian H in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples.
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