Highly resolved spectral functions of two-dimensional systems with neural quantum states
arXiv:2303.08184 · doi:10.1103/PhysRevLett.131.046501
Abstract
Spectral functions are central to link experimental probes to theoretical models in condensed matter physics. However, performing exact numerical calculations for interacting quantum matter has remained a key challenge especially beyond one spatial dimension. In this work, we develop a versatile approach using neural quantum states to obtain spectral properties based on simulations of the dynamics of excitations initially localized in real or momentum space. We apply this approach to compute the dynamical structure factor in the vicinity of quantum critical points (QCPs) of different two-dimensional quantum Ising models, including one that describes the complex density wave orders of Rydberg atom arrays. When combined with deep network architectures we find that our method reliably describes dynamical structure factors of arrays with up to spins, including the diverging time scales at critical points. Our approach is broadly applicable to interacting quantum lattice models in two dimensions and consequently opens up a route to compute spectral properties of correlated quantum matter in yet inaccessible regimes.
Published version
References in corpus (18)
- Real time evolution using the density matrix renormalization group
- Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
- Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator
- Probing Topological Spin Liquids on a Programmable Quantum Simulator
- Programmable quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms
- QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains
- Time-evolving a matrix product state with long-ranged interactions
- Prediction of Toric Code Topological Order from Rydberg Blockade
- Quantum phase transition dynamics in the two-dimensional transverse-field Ising model
- Finite-size scaling at first-order quantum transitions
- Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry
- Efficient Tensor Network ansatz for high-dimensional quantum many-body problems
- Optimizing Design Choices for Neural Quantum States
- Investigating ultrafast quantum magnetism with machine learning
- Supermagnonic propagation in two-dimensional antiferromagnets
- Chebyshev expansion of spectral functions using restricted Boltzmann machines
- jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
- Critical Behaviour of One-particle Spectral Weights in the Transverse Ising Model
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- A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States
- Neural-network quantum states for many-body physics
- Neural network approach to quasiparticle dispersions in doped antiferromagnets
- Neural Projected Quantum Dynamics: a systematic study
- Fine-tuning Neural Network Quantum States
- Spectral functions with infinite projected entangled-pair states
- Simplicity of mean-field theories in neural quantum states
- Design principles of deep translationally-symmetric neural quantum states for frustrated magnets
- Simulating dynamics of the two-dimensional transverse-field Ising model: a comparative study of large-scale classical numerics
- Adaptive quantum dynamics with the time-dependent variational Monte Carlo method
- Machine Learning Green's Functions of Strongly Correlated Hubbard Models
- Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice