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Sample generation for the spin-fermion model using neural networks
[article]
2022
arXiv
pre-print
Quantum Monte-Carlo simulations of hybrid quantum-classical models such as the double exchange Hamiltonian require calculating the density of states of the quantum degrees of freedom at every step. Unfortunately, the computational complexity of exact diagonalization grows 𝒪 (N^3) as a function of the system's size N, making it prohibitively expensive for any realistic system. We consider leveraging data-driven methods, namely neural networks, to replace the exact diagonalization step in order
arXiv:2206.07753v1
fatcat:7a5io2dgbvahvbr765hemsrhxu