The authors show that stochastic reconfiguration is L2 gradient descent on the unit sphere, and they derive projected inverse iteration, a shifted inverse iteration method for neural-network wavefunctions, with faster convergence on spin models.
Algorithms for variational Monte Carlo calculations of fermion projected entangled pair states in the swap gates formulation and the detailed balance of tensor network sequential sampling
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abstract
In recent years, the variational Monte Carlo (VMC) calculations of projected entangled pair states (PEPS) has emerged as a competitive method for computing the ground states of many-body quantum systems. This method is particularly important for fermion systems where sign problems are abundant. We derive and explain the algorithms for the VMC calculations of fermion PEPS in the swap gates formulation. As a separate key result, we prove the detailed balance of sequential sampling of tensor networks.
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Functional Neural Wavefunction Optimization
The authors show that stochastic reconfiguration is L2 gradient descent on the unit sphere, and they derive projected inverse iteration, a shifted inverse iteration method for neural-network wavefunctions, with faster convergence on spin models.