Tensor network simulations of quasi-GPDs in the massive Schwinger model

• URL: http://arxiv.org/abs/2511.17752v1
• Date: Fri, 21 Nov 2025 20:06:09 GMT
• Title: Tensor network simulations of quasi-GPDs in the massive Schwinger model
• Authors: Sebastian Grieninger, Jake Montgomery, Felix Ringer, Ismail Zahed,
• Abstract summary: Generalized Parton Distribution functions encode the internal structure of hadrons in terms of quark and gluon degrees of freedom.<n>We present the first nonperturbative study of quasi-GPDs in the massive Schwinger model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generalized Parton Distribution functions (GPDs) are off-diagonal light-cone matrix elements that encode the internal structure of hadrons in terms of quark and gluon degrees of freedom. In this work, we present the first nonperturbative study of quasi-GPDs in the massive Schwinger model, quantum electrodynamics in 1+1 dimensions (QED2), within the Hamiltonian formulation of lattice field theory. Quasi-distributions are spatial correlation functions of boosted states, which approach the relevant light-cone distributions in the luminal limit. Using tensor networks, we prepare the first excited state in the strongly coupled regime and boost it to close to the light-cone on lattices of up to 400 lattice sites. We compute both quasi-parton distribution functions and, for the first time, quasi-GPDs, and study their convergence for increasingly boosted states. In addition, we perform analytic calculations of GPDs in the two-particle Fock-space approximation and in the Reggeized limit, providing qualitative benchmarks for the tensor network results. Our analysis establishes computational benchmarks for accessing partonic observables in low-dimensional gauge theories, offering a starting point for future extensions to higher dimensions, non-Abelian theories, and quantum simulations.

Related papers

• Pruning at Initialisation through the lens of Graphon Limit: Convergence, Expressivity, and Generalisation [16.628325681877556]
  Pruning at Initialisation methods discover sparse, trainableworks before training, but their theoretical mechanisms remain elusive.<n>In this work, we connect discrete intrinsic prunings to graph limit theory via graphons, establishing the graphon limit of PaI masks.<n>We derive two fundamental theoretical results: (i) a Universal Approximation Theorem for sparse networks that depends only on the dimension of active coordinate subspaces; and (ii) a Graphon-NTK generalisation bound demonstrating how the limit graphon geometry to align with informative features.
  arXiv  Detail & Related papers  (2026-02-06T13:02:47Z)
• Quantum simulation of deep inelastic scattering in the Schwinger model [0.0]
  Hadronic tensors encode the nonperturbative structure of hadrons probed in deep inelastic scattering (DIS)<n>Our study demonstrates that quantum simulation offers a viable complementary pathway towards the evaluation of real-time observables relevant for hadronic structure.
  arXiv  Detail & Related papers  (2025-12-19T21:02:01Z)
• Sample-Based Krylov Quantum Diagonalization for the Schwinger Model on Trapped-Ion and Superconducting Quantum Processors [26.315169722500556]
  We apply the recently proposed Sample-based Krylov Quantum Diagonalization (SKQD) method to lattice gauge theories.<n>We study the dependence of the ground-state energy and particle number on the value of the $theta$-term, accurately capturing the model's phase structure.<n>We show that SKQD substantially reduces the effective Hilbert space, and although the Krylov space dimension still scales exponentially, the slower growth underscores its promise for simulating lattice gauge theories in larger volumes.
  arXiv  Detail & Related papers  (2025-10-30T19:21:06Z)
• Renormalized dual basis for scalable simulations of 2+1D compact quantum electrodynamics [0.0]
  We introduce a new truncation scheme -- here renamed as Renormalized Dual Basis (RDB) -- based on the resolution of the single-plaquette problem.<n>We show that the RDB provides an efficient description across all coupling regimes.
  arXiv  Detail & Related papers  (2025-10-21T12:51:04Z)
• Parton Distribution Functions in the Schwinger model from Tensor Network States [0.08035416719640157]
  We propose implementing the light-front Wilson line within the Hamiltonian formalism using tensor network techniques.<n>We present accurate results for the fermion PDF of the vector meson at varying fermion masses, obtained from first principle calculations directly in Minkowski space.
  arXiv  Detail & Related papers  (2025-04-10T07:10:53Z)
• Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks [12.001676605529626]
  We explore the approximation theory of functions defined on graphs. We establish a framework to assess the lower bounds of approximation for target functions using Graph Convolutional Networks (GCNs) We show how the high-frequency energy of the output decays, an indicator of over-smoothing, in GCNs.
  arXiv  Detail & Related papers  (2024-07-01T13:35:53Z)
• Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
  We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials. It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
  arXiv  Detail & Related papers  (2024-02-22T16:33:48Z)
• Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory [50.24983453990065]
  An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers. We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states. By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
  arXiv  Detail & Related papers  (2024-01-31T15:50:50Z)
• Quantum tomography of helicity states for general scattering processes [55.2480439325792]
  Quantum tomography has become an indispensable tool in order to compute the density matrix $rho$ of quantum systems in Physics. We present the theoretical framework for reconstructing the helicity quantum initial state of a general scattering process.
  arXiv  Detail & Related papers  (2023-10-16T21:23:42Z)
• Convex Analysis of the Mean Field Langevin Dynamics [49.66486092259375]
  convergence rate analysis of the mean field Langevin dynamics is presented. $p_q$ associated with the dynamics allows us to develop a convergence theory parallel to classical results in convex optimization.
  arXiv  Detail & Related papers  (2022-01-25T17:13:56Z)
• Conformal field theory from lattice fermions [77.34726150561087]
  We provide a rigorous lattice approximation of conformal field theories given in terms of lattice fermions in 1+1-dimensions. We show how these results lead to explicit error estimates pertaining to the quantum simulation of conformal field theories.
  arXiv  Detail & Related papers  (2021-07-29T08:54:07Z)
• Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes [144.6048446370369]
  Graph convolutional neural networks(GCNs) have recently demonstrated promising results on graph-based semi-supervised classification. We propose a GP regression model via GCNs(GPGC) for graph-based semi-supervised learning. We conduct extensive experiments to evaluate GPGC and demonstrate that it outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-26T10:02:32Z)

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.