Time evolution of the quantum Ising model in two dimensions using Tree Tensor Networks
- URL: http://arxiv.org/abs/2505.07612v1
- Date: Mon, 12 May 2025 14:34:28 GMT
- Title: Time evolution of the quantum Ising model in two dimensions using Tree Tensor Networks
- Authors: Wladislaw Krinitsin, Niklas Tausendpfund, Markus Heyl, Matteo Rizzi, Markus Schmitt,
- Abstract summary: Tree Network (TTN) states are used to solve the dynamics of the quantum Ising model in two dimensions.<n>We show that TTNs reproduce analytically known, but non-trivial and physically interesting results, for lattices up to $16 times 16$ sites.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The numerical simulation of two-dimensional quantum many-body systems away from equilibrium constitutes a major challenge for all known computational methods. We investigate the utility of Tree Tensor Network (TTN) states to solve the dynamics of the quantum Ising model in two dimensions. Within the perturbative regime of small transverse fields, TTNs faithfully reproduce analytically known, but non-trivial and physically interesting results, for lattices up to $16 \times 16$ sites. Limitations of the method related to the rapid growth of entanglement entropy are explored within more general, paradigmatic quench settings. We provide and discuss comprehensive benchmarks regarding the benefit of \emph{GPU} acceleration and the impact of using local operator sums on the performance.
Related papers
- Solving the Hubbard model with Neural Quantum States [66.55653324211542]
We study the state-of-the-art results for the doped two-dimensional (2D) Hubbard model.<n>We find different attention heads in the NQS ansatz can directly encode correlations at different scales.<n>Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.
arXiv Detail & Related papers (2025-07-03T14:08:25Z) - Hamiltonian Lattice Gauge Theories: emergent properties from Tensor Network methods [0.0]
This thesis develops advanced Network (TN) methods to address Hamiltonian Lattice Theories (LGTs)<n>A novel dressed-site formalism is introduced, enabling efficient truncation of gauge fields.<n>These advances open current and future development pathways toward optimized, efficient, and faster simulations on scales comparable to Monte Carlo state-of-the-art.
arXiv Detail & Related papers (2025-01-19T17:09:57Z) - Time-dependent Neural Galerkin Method for Quantum Dynamics [42.81677042059531]
We introduce a classical computational method for quantum dynamics that relies on a global-in-time variational principle.<n>Our scheme computes the entire state trajectory over a finite time window by minimizing a loss function that enforces the Schr"odinger's equation.<n>We showcase the method by simulating global quantum quenches in the paradigmatic Transverse-Field Ising model in both 1D and 2D.
arXiv Detail & Related papers (2024-12-16T13:48:54Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum
Problems [42.375903320536715]
The entropic fictitious play (EFP) is a recently proposed algorithm that minimizes the sum of a convex functional and entropy in the space of measures.
We provide a concise primal-dual analysis of EFP in the setting where the learning problem exhibits a finite-sum structure.
arXiv Detail & Related papers (2023-03-06T08:05:08Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Neural network enhanced measurement efficiency for molecular
groundstates [63.36515347329037]
We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians.
We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
arXiv Detail & Related papers (2022-06-30T17:45:05Z) - Neural-Network Quantum States for Periodic Systems in Continuous Space [66.03977113919439]
We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity.
For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles.
In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
arXiv Detail & Related papers (2021-12-22T15:27:30Z) - Dynamics of two-dimensional open quantum lattice models with tensor
networks [0.0]
We develop a tensor network method, based on an infinite Projected Entangled Pair Operator (iPEPO) ansatz, applicable directly in the thermodynamic limit.
We consider dissipative transverse quantum Ising and driven-dissipative hard core boson models in non-mean field limits.
Our method enables to study regimes which are accessible to current experiments but lie well beyond the applicability of existing techniques.
arXiv Detail & Related papers (2020-12-22T18:24:20Z) - Efficient Tensor Network ansatz for high-dimensional quantum many-body
problems [0.0]
We introduce a novel tensor network structure augmenting the well-established Tree Network representation of a quantum many-body wave function.
We benchmark this novel approach against paradigmatic two-dimensional spin models demonstrating unprecedented precision and system sizes.
arXiv Detail & Related papers (2020-11-16T19:00:04Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.