Universal Neural Propagator: Learning Time Evolution in Many-Body Quantum Systems

• URL: http://arxiv.org/abs/2605.05299v1
• Date: Wed, 06 May 2026 18:00:01 GMT
• Title: Universal Neural Propagator: Learning Time Evolution in Many-Body Quantum Systems
• Abstract summary: We introduce the Universal Neural Propagator (UNP), a single, unified model that learns the functional mapping from driving protocols to time-evolution propagators.<n>We benchmark on a two-dimensional driven Ising model and demonstrate the UNP's accuracy and transferability across product and entangled initial states.
• Score: 8.415180820101964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional approaches to simulating quantum many-body dynamics produce a single trajectory: if the Hamiltonian or the initial state is changed, the computation must be re-performed. Recent efforts toward foundation models have begun to address this limitation, yet existing methods transfer across either Hamiltonians or initial states, but not both. In this work, we introduce the Universal Neural Propagator (UNP), a single, unified model that learns the functional mapping from driving protocols to time-evolution propagators. Trained in an entirely self-supervised way, a single UNP model predicts dynamics across a function space of driving protocols and an exponentially large Hilbert space of initial states simultaneously. We benchmark on a two-dimensional driven Ising model and demonstrate the UNP's accuracy and transferability across product and entangled initial states, as well as for both in- and out-of-distribution driving protocols. The UNP remains accurate at system sizes beyond exact diagonalization, and can be efficiently fine-tuned across all initial states using observable data. By shifting the object of learning from quantum states to operators, this work opens a route toward transferable simulation of driven quantum matter.

Related papers

• Quantum Incremental Learning with Mixed State Prototypes [53.319426778720434]
  We introduce a novel quantum incremental learning framework based on trainable mixed-state prototypes.<n>Our model achieves high-dimensional feature concentration using a minimal number of qubits.
  arXiv  Detail & Related papers  (2026-08-11T04:22:42Z)
• Neural Operator Quantum State: A Foundation Model for Quantum Dynamics [8.415180820101964]
  We introduce the Neural Operator Quantum State (NOQS) as a foundation model for quantum dynamics.<n>Our approach aims to emphlearn the solution operator that maps entire driving protocols to time-evolved quantum states.<n>We validate NOQS on the two-dimensional Ising model with time-dependent longitudinal and transverse fields.
  arXiv  Detail & Related papers  (2026-03-26T06:10:14Z)
• Learning Hamiltonians for solid-state quantum simulators [0.0]
  We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems.<n>Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure.
  arXiv  Detail & Related papers  (2026-03-03T11:37:43Z)
• Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models [60.87795376541144]
  A world model is a neural network capable of predicting an agent's next state given past states and actions.<n>During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations.<n>We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing.
  arXiv  Detail & Related papers  (2024-09-25T06:48:25Z)
• 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)
• Learning effective Hamiltonians for adaptive time-evolution quantum algorithms [0.08796261172196743]
  Digital quantum simulation of many-body dynamics relies on Trotterization to decompose the target time evolution into elementary quantum gates. Recent advances have outlined protocols enabling more efficient adaptive Trotter protocols. We propose to use quantum Hamiltonian learning to numerically obtain the effective Hamiltonian and apply it on the recently introduced ADA-Trotter algorithm.
  arXiv  Detail & Related papers  (2024-06-10T11:50:38Z)
• RealTimeTransport: An open-source C++ library for quantum transport simulations in the strong coupling regime [0.0]
  RealTimeTransport is a new open-source C++ library that enables the computation of both stationary and transient transport observables. It computes the Nakajima-Zwanzig memory kernels for both dynamics and transport in real-time. This paper provides an overview of the theory, the architecture of RealTimeTransport and practical demonstrations of the currently implemented methods.
  arXiv  Detail & Related papers  (2024-05-28T16:36:22Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases [66.61789780666727]
  We show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. We also offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states. Our model yields a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-08-25T07:15:58Z)
• 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)
• Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics [97.38308257547186]
  Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and material models. We argue that the governing PDEs are often well-known and should be explicitly enforced rather than learned. We introduce a new framework termed "Neural Constitutive Laws" (NCLaw) which utilizes a network architecture that strictly guarantees standard priors.
  arXiv  Detail & Related papers  (2023-04-27T17:42:24Z)
• Shadow tomography from emergent state designs in analog quantum simulators [0.0]
  We introduce a method that allows one to infer many properties of a quantum state using only global control. We show that when the unitary is sufficiently entangling, a universal relationship between the statistics of the measurement outcomes and properties of the state emerges.
  arXiv  Detail & Related papers  (2022-12-05T19:04:21Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)

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.