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Paths Towards Time Evolution with Larger Neural-Network Quantum States

arXiv ยท

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Abstract
In recent years, the neural-network quantum states method has beeninvestigated to study the ground state and the time evolution of many-bodyquantum systems. Here we expand on the investigation and consider a quantumquench from the paramagnetic to the anti-ferromagnetic phase in the tiltedIsing model. We use two types of neural networks, a restricted Boltzmannmachine and a feed-forward neural network. We show that for both types ofnetworks, the projected time-dependent variational Monte Carlo (p-tVMC) methodperforms better than the non-projected approach. We further demonstrate thatone can use K-FAC or minSR in conjunction with p-tVMC to reduce thecomputational complexity of the stochastic reconfiguration approach, thusallowing the use of these techniques for neural networks with more parameters.
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