Scalable quantum simulation of continuous-time generative models via tensor networks

• URL: http://arxiv.org/abs/2608.21700v1
• Date: Sat, 22 Aug 2026 00:24:06 GMT
• Title: Scalable quantum simulation of continuous-time generative models via tensor networks
• Abstract summary: Continuous-time flow and diffusion models are widely used across many application domains.<n>Wavefunction flows target this cost by recasting learned transport as unitary evolution.<n>We present the first numerical study of these flows, in which we represent time-dependent potentials and states as tensor networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein folding to emerging adoption for modeling language, time series, and quantum states. After training, inferring statistical properties from continuous-time models is costly. Wavefunction flows target this cost by recasting learned transport as unitary evolution, whose final Born distribution approximates the target distribution. This prepares a coherent amplitude encoding (a qsample) that can be post-processed by quantum algorithms offering a quadratic advantage over Monte Carlo sampling. We present the first numerical study of these flows, in which we represent time-dependent potentials and states as tensor networks. At spatial dimension $d=8$, storage falls by $\sim 10^7\times$ relative to the dense grid of $N^d$ points, and evolution wall-clock time falls by $\gtrsim 10^3\times$ against a baseline extrapolated from the measured $d\le 5$ scaling. We validate our pipeline by reproducing the $O(1/\sqrt{p_{\rm rare}})$ scaling of rare-event sampling.

Related papers

• Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling [55.388363730120325]
  We parallelize the mainstream $$-leaping algorithm for absorbing discrete diffusion in a Continuous-Time Markov Chain (CTMC) framework.<n>By leveraging the continuous-time integral form of the $$-leaping algorithm and the Picard method, we achieve parallel-in-time sampling acceleration.<n>Our research expands the potential of discrete diffusion models for efficient parallel inference, with broader implications for applications such as molecular structure and language generation.
  arXiv  Detail & Related papers  (2026-07-01T10:59:33Z)
• One-Step Generative Modeling via Wasserstein Gradient Flows [56.266175641614986]
  W-Flow is a framework for training a generator that transforms samples from a simple reference distribution into samples from a target data distribution in a single step.<n>We set a new state of the art for one-step ImageNet 256$times$256 generation, achieving 1.29 FID, with improved mode coverage and domain transfer.
  arXiv  Detail & Related papers  (2026-05-12T08:29:44Z)
• Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling [8.539489108260872]
  Pre-Trajectory Sampling with Batched Execution (PTSBE) has dramatically increased the data collection rate of these methods.<n>While PTSBE has demonstrated data collection speedups of over $106 times$, tensor network implementations only achieved $sim 15 times$ speedup.<n>We develop 1) error-independent unified path variation, 2) non-degenerate tensor network sampling, and 3) a flexible/optimized contraction framework.
  arXiv  Detail & Related papers  (2026-04-09T17:02:18Z)
• Multi-timescale time encoding for CNN prediction of Fenna-Matthews-Olson energy-transfer dynamics [4.040694711418461]
  We develop a non-recursive convolutional neural networks that maps system parameters and a redundant time encoding directly to excitation-energy-transfer populations.<n>The network accurately predicts $0sim100 ps$ dynamics across a range of reorganization energies, bath rates, and temperatures.
  arXiv  Detail & Related papers  (2025-03-21T12:40:39Z)
• Computing exact moments of local random quantum circuits via tensor networks [0.40498500266986387]
  A basic primitive in quantum information is the computation of the moments $mathbbE_U[rm Tr[Urho Udagger O]t]$. In this work, we propose to estimate the moments via tensor networks, where the local gates moment operators are mapped to small dimensional tensors acting on their local commutant bases.
  arXiv  Detail & Related papers  (2024-03-04T03:50:31Z)
• A Dynamical Model of Neural Scaling Laws [79.59705237659547]
  We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
  arXiv  Detail & Related papers  (2024-02-02T01:41:38Z)
• Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models [49.81937966106691]
  We develop a suite of non-asymptotic theory towards understanding the data generation process of diffusion models. In contrast to prior works, our theory is developed based on an elementary yet versatile non-asymptotic approach.
  arXiv  Detail & Related papers  (2023-06-15T16:30:08Z)
• Computing high-dimensional optimal transport by flow neural networks [19.859984725284896]
  This work proposes to compute the dynamic OT between two arbitrary distributions $P$ and $Q$ by optimizing a flow model.<n>Our method learns the dynamic OT by finding an invertible flow that minimizes the transport cost.<n>The effectiveness of the proposed model on high-dimensional data is demonstrated by strong empirical performance on OT baselines, image-to-image translation, and high-dimensional DRE.
  arXiv  Detail & Related papers  (2023-05-19T17:48:21Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Hierarchical autoregressive neural networks for statistical systems [0.05156484100374058]
  We propose a hierarchical association of physical degrees of freedom, for instance spins, to neurons which replaces it with the scaling with the linear extent $L$ of the system. We demonstrate our approach on the two-dimensional Ising model by simulating lattices of various sizes up to $128 times 128$ spins, with time benchmarks reaching lattices of size $512 times 512$.
  arXiv  Detail & Related papers  (2022-03-21T13:55:53Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)
• Gravitational-wave parameter estimation with autoregressive neural network flows [0.0]
  We introduce the use of autoregressive normalizing flows for rapid likelihood-free inference of binary black hole system parameters from gravitational-wave data with deep neural networks. A normalizing flow is an invertible mapping on a sample space that can be used to induce a transformation from a simple probability distribution to a more complex one. We build a more powerful latent variable model by incorporating autoregressive flows within the variational autoencoder framework.
  arXiv  Detail & Related papers  (2020-02-18T15:44:04Z)

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.