Many-Body Quantum States with Exact Conservation of Non-Abelian and Lattice Symmetries through Variational Monte Carlo
arXiv:2104.14869 · doi:10.1103/PhysRevB.104.045123
Abstract
Optimization of quantum states using the variational principle has recently seen an upsurge due to developments of increasingly expressive wave functions. In order to improve on the accuracy of the ansätze, it is a time-honored strategy to impose the systems' symmetries. We present an ansatz where global non-abelian symmetries are inherently embedded in its structure. We extend the model to incorporate lattice symmetries as well. We consider the prototypical example of the frustrated two-dimensional - model on a square lattice, for which eigenstates have been hard to model variationally. Our novel approach guarantees that the obtained ground state will have total spin zero. Benchmarks on the 2D - model demonstrate its state-of-the-art performance in representing the ground state. Furthermore, our methodology permits to find the wave functions of excited states with definite quantum numbers associated to the considered symmetries (including the non-abelian ones), without modifying the architecture of the network.
References in corpus (24)
- Adam: A Method for Stochastic Optimization
- The density-matrix renormalization group in the age of matrix product states
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Quantum Spin Liquids
- Tensor networks for complex quantum systems
- Tensor network decompositions in the presence of a global symmetry
- Plaquette Ordered Phase and Quantum Phase Diagram in the Spin-1/2 J1-J2 Square Heisenberg Model
- Green Function Monte Carlo with Stochastic Reconfiguration
- Symmetries and many-body excited states with neural-network quantum states
- Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States
- Resonating Valence Bond Wave Functions for Strongly Frustrated Spin Systems
- Dimer Order with Striped Correlations in the J_1-J_2 Heisenberg Model
- Direct evidence for a gapless spin liquid by frustrating Néel antiferromagnetism
- New valence bond crystal phase of a frustrated spin-1/2 square lattice antiferromagnet
- NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
- Tensor network states and algorithms in the presence of a global SU(2) symmetry
- Restricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries
- Neural Gutzwiller-projected variational wave functions
- Symmetry-adapted variational quantum eigensolver
- Gapless spin liquid ground state of spin-1/2 - Heisenberg model on square lattices
- Spin-1/2 J1-J2 Heisenberg antiferromagnet on a square lattice: a plaquette renormalized tensor network study
- Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry
- A programming guide for tensor networks with global symmetry
- The Three-Legged Tree Tensor Networks with SU(2)- and molecular point group symmetry
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- The quantum Gaussian process state: A kernel-inspired state with quantum support data
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- Approximately-symmetric neural networks for quantum spin liquids
- Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
- SU(d)-Symmetric Random Unitaries: Quantum Scrambling, Error Correction, and Machine Learning
- Towards Symmetry-Aware Efficient Simulation of Quantum Systems and Beyond
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