Understanding and eliminating spurious modes in variational Monte Carlo using collective variables
This addresses a specific issue in VMC for quantum physics simulations, offering a robust training method that is incremental but broadly applicable.
The paper tackles the problem of unreliable wave function approximations in variational Monte Carlo (VMC) due to spurious modes, which cause random energy spikes during training, and demonstrates that a collective-variable-based penalization prevents these modes and improves energy accuracy.
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years. However, as we demonstrate in the context of the periodic Heisenberg spin chain, this approach can produce unreliable wave function approximations. One of the most obvious signs of failure is the occurrence of random, persistent spikes in the energy estimate during training. These energy spikes are caused by regions of configuration space that are over-represented by the wave function density, which are called ``spurious modes'' in the machine learning literature. After exploring these spurious modes in detail, we demonstrate that a collective-variable-based penalization yields a substantially more robust training procedure, preventing the formation of spurious modes and improving the accuracy of energy estimates. Because the penalization scheme is cheap to implement and is not specific to the particular model studied here, it can be extended to other applications of VMC where a reasonable choice of collective variable is available.