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Neural-network quantum states have shown great potential for the study of many-body quantum systems.
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Here we have compared both of the codes with the same set of parameters using restricted Boltzmann machine and sampling with the Metropolis-Hastings algorithm that flips a random spin. One should notice that the NetKet implementation uses a slightly different version of the RBM than the one presented here, where the wave function is directly given by ψ ( 𝒙 , 𝜽 ) = p R B M m ( 𝒙 , 𝜽 ) \psi(\bm{x},\bm{\theta})=p_{RBM}^{m}(\bm{x};\bm{\theta}) . For a fair comparison of the performance of both codes, we used this definition instead of the one presented in Sec. II
Cited in the paper.
As a reference, at the time of writing, the cost of each processor and cost of the graphics processing units is around 500 USD
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Here, and for the Heisenberg model discussed in the next paragraph, we have used the weights of a system with 64 64 spins from the respective transfer learning protocol, as in Fig. 4
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S. Das Sarma, D.-L. Deng, and L.-M. Duan, Physics Today 72
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K. Choo, T. Neupert, and G. Carleo, arxiv:1903.06713 (2019)
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