Entanglement Entropy in Quantum Networks with Tunable Geometry

• URL: http://arxiv.org/abs/2607.16394v1
• Date: Fri, 17 Jul 2026 18:00:01 GMT
• Title: Entanglement Entropy in Quantum Networks with Tunable Geometry
• Abstract summary: We study quantum hopping on a random graph model with an effective range parameter.<n> tuning it, we interpolate between nearest-neighbor and all-to-all graphs, identifying a new localization regime.<n>This provides a model of Anderson localization induced by structural disorder, with implications for amorphous materials and tunable-range analog quantum simulators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum many-body Hamiltonians with two-body interactions can be represented by graphs, with sites as nodes and two-site couplings as edges. We investigate how the geometry of these graphs affects transport and entanglement growth. To do so, we study dynamics for quantum hopping on a random graph model with an effective range parameter. Tuning it, we interpolate between nearest-neighbor and all-to-all graphs, identifying a new localization regime that is robust in both single-particle and finite-density cases. This provides a model of Anderson localization induced by structural disorder, with implications for amorphous materials and tunable-range analog quantum simulators.

Related papers

• Interaction geometry and ground-state properties of sparse quantum lattice models [2.2933318919445607]
  We show how symmetry and frustration in graph connectivity can drive, suppress, and reshape ground-state phase transitions.<n>We find that phase structure and criticality are governed by the same effective-geometry principle, unifying our framework for experimentally motivated long-range quantum systems.
  arXiv  Detail & Related papers  (2026-06-18T15:44:34Z)
• Symmetry-protected topology and deconfined solitons in a multi-link $\mathbb{Z}_2$ gauge theory [45.88028371034407]
  We study a $mathbbZ$ lattice gauge theory defined on a multi-graph with links that can be visualized as great circles of a spherical shell.<n>We show that this leads to state-dependent tunneling amplitudes underlying a phenomenon analogous to the Peierls instability.<n>By performining a detailed analysis based on matrix product states, we prove that charge deconfinement emerges as a consequence of charge-fractionalization.
  arXiv  Detail & Related papers  (2026-03-02T22:59:25Z)
• Entanglement in Quantum Systems Based on Directed Graphs [0.0]
  We investigate entanglement properties of quantum states associated with directed graphs.<n>Using a measure derived from the Fubini-Study metric, we quantitatively relate multipartite entanglement to the local connectivity of the graph.
  arXiv  Detail & Related papers  (2025-09-05T16:17:03Z)
• Exponential onset of scalable entanglement via twist-and-turn dynamics in XY models [41.94295877935867]
  We show that the so-called twist-and-turn" (TaT) dynamics can offer an important resource to reach scalable multipartite entanglement.<n>For dipolar interactions, the entanglement dynamics at intermediate times is completely at odds with thermalization.
  arXiv  Detail & Related papers  (2025-07-10T22:34:44Z)
• Entangled states from sparsely coupled spins for metrology with neutral atoms [0.0]
  We show that optimal states for quantum sensing can be generated with sparse interaction graphs featuring only a logarithmic number of couplings per particle.<n>The resulting sparse coupling graphs and protocol can also be efficiently implemented using dynamic reconfiguration of atoms in optical tweezers.
  arXiv  Detail & Related papers  (2024-12-13T09:53:56Z)
• Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning [77.1421343649344]
  We propose a generalization of Transformers towards operating entirely on the product of constant curvature spaces. We also provide a kernelized approach to non-Euclidean attention, which enables our model to run in time and memory cost linear to the number of nodes and edges.
  arXiv  Detail & Related papers  (2023-09-08T02:44:37Z)
• Message-Passing Neural Quantum States for the Homogeneous Electron Gas [41.94295877935867]
  We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions.
  arXiv  Detail & Related papers  (2023-05-12T04:12:04Z)
• Quantum simulator of link models using spinor dipolar ultracold atoms [0.0]
  Scheme for the quantum simulation of quantum link models in two-dimensional lattices is presented. We derivation the parameters of the quantum link models by means of two different approaches. The extension to three-dimensional lattices is presented, and its subtleties are pointed out.
  arXiv  Detail & Related papers  (2022-10-26T16:36:05Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• An Interpretable Graph Generative Model with Heterophily [38.59200985962146]
  We propose the first edge-independent graph generative model that is expressive enough to capture heterophily. Our experiments demonstrate the effectiveness of our model for a variety of important application tasks.
  arXiv  Detail & Related papers  (2021-11-04T17:34:39Z)
• GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning [16.484048833163282]
  We introduce a novel Transformer-based framework for molecular representation learning, named the Geometry-aware Transformer (GeoT) GeoT learns molecular graph structures through attention-based mechanisms specifically designed to offer reliable interpretability, as well as molecular property prediction. Our comprehensive experiments, including an empirical simulation, reveal that GeoT effectively learns the chemical insights into molecular structures, bridging the gap between artificial intelligence and molecular sciences.
  arXiv  Detail & Related papers  (2021-06-29T15:47:18Z)
• Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models [16.987004075528606]
  We develop a general approach to constructing gauge invariant or anyonic symmetric autoregressive neural network quantum states. We prove that our methods can provide exact representation for the ground and excited states of the 2D and 3D toric codes. We variationally optimize our symmetry incorporated autoregressive neural networks for ground states as well as real-time dynamics for a variety of models.
  arXiv  Detail & Related papers  (2021-01-18T18:55:21Z)
• 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)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

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.