Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks
arXiv:2003.06228
Abstract
Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size. Finding efficient compressions of the wavefunction is key to building scalable models. Here, we introduce iterative retraining, an approach for simulating bulk quantum systems that uses recurrent neural networks (RNNs). By mapping translations in the lattice vector to the time index of an RNN, we are able to efficiently capture the near translational invariance of large lattices. We show that we can use this symmetry mapping to simulate very large systems in one and two dimensions. We do so by 'growing' our model, iteratively retraining the same model on progressively larger lattices until edge effects become negligible. We argue that this scheme generalizes more naturally to higher dimensions than Density Matrix Renormalization Group.
arXiv comment7 pages, 4 figures
References in corpus (17)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Anomalous Hall effect
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Pixel Recurrent Neural Networks
- DRAW: A Recurrent Neural Network For Image Generation
- Many-body quantum state tomography with neural networks
- Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions
- Machine learning quantum phases of matter beyond the fermion sign problem
- Reconstructing quantum states with generative models
- Recurrent Neural Network Wave Functions
- Deep autoregressive models for the efficient variational simulation of many-body quantum systems
- Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States
- Solving Statistical Mechanics Using Variational Autoregressive Networks
- Isometric Tensor Network States in Two Dimensions
- Machine learning technique to find quantum many-body ground states of bosons on a lattice
- Gradient optimization of finite projected entangled pair states
- A generalized Lanczos method for systematic optimization of tensor network states
Cited by in corpus (15)
- Machine Learning on Neutron and X-Ray Scattering
- Variational Neural Annealing
- A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States
- Advances in Quantum Deep Learning: An Overview
- U(1) symmetric recurrent neural networks for quantum state reconstruction
- Group Convolutional Neural Networks Improve Quantum State Accuracy
- Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz
- Simulating disordered quantum systems via dense and sparse restricted Boltzmann machines
- Gaussian Process States: A data-driven representation of quantum many-body physics
- A Bayesian Inference Framework for Compression and Prediction of Quantum States
- Neural networks in quantum many-body physics: a hands-on tutorial
- Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
- Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
- Kinetic samplers for neural quantum states
- Quantum-Inspired Tempering for Ground State Approximation using Artificial Neural Networks