Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
This work addresses efficiency challenges in quantum many-body physics simulations, offering incremental improvements for researchers using NQS methods.
The authors tackled the computational cost and training fluctuations in neural-network quantum states (NQS) by introducing an adaptive scheme using recurrent neural networks (RNNs), which reduced computational cost and improved variational calculations for ground states in quantum many-body models.
Neural-network quantum states (NQS) are powerful neural-network ansätzes that have emerged as promising tools for studying quantum many-body physics through the lens of the variational principle. These architectures are known to be systematically improvable by increasing the number of parameters. Here we demonstrate an Adaptive scheme to optimize NQSs, through the example of recurrent neural networks (RNN), using a fraction of the computation cost while reducing training fluctuations and improving the quality of variational calculations targeting ground states of prototypical models in one- and two-spatial dimensions. This Adaptive technique reduces the computational cost through training small RNNs and reusing them to initialize larger RNNs. This work opens up the possibility for optimizing graphical processing unit (GPU) resources deployed in large-scale NQS simulations.