The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.
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Denoising Diffusion Probabilistic Models
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We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion
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- abstract We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score
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citing papers explorer
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Generating quantum ensembles via reverse-time quantum diffusions
The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.
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Generative models on phase space
Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.
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Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents
MF-PID turns independent diffusion samples into mean-field interacting agents, proving that quadratic interactions yield exact linear mean interpolation and delivering 19-24% energy savings in demand-response control.
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Diffusion-warm sampling of the XY model enables fast thermalization at scale
A temperature-conditioned diffusion model trained on small XY lattices produces accurate larger-lattice samples and cuts MCMC thermalization time by roughly 10x.
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Lie Group Diffusion Models for Hardware-Aware Quantum Circuit Synthesis
Lie group diffusion models combine a discrete circuit skeleton selector with continuous diffusion on SU(2) ≃ S³ to synthesize hardware-aware quantum circuits, outperforming baselines on three-qubit Hamiltonian simulation targets.
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Sampling the Schwinger Model with Gauge-Equivariant Diffusion
A gauge-equivariant diffusion model samples Schwinger model configurations, yielding unbiased observables matching MCMC and qualitatively less topological freezing than HMC.
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Constrained Diffusion Models with Primal-Dual Inference
Develops primal-dual inference (PDI) that jointly infers optimal primal distributions and dual multipliers during diffusion sampling using a dual-conditioned score network.
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Improving Robotic Generalist Policies via Flow Reversal Steering
Flow Reversal Steering steers flow matching generalist policies by reversing suboptimal actions to nearby better modes, enabling improved zero-shot control, quick distillation, and RL bootstrapping in robotic manipulation.
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Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
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Spectrally Regularized Latent Flow Matching for Turbulence Generation
Spectrally regularized compression in latent flow matching raises retained deep-dissipation spectral power from 20% to 79% in generated turbulence on a 256^2 DNS dataset at Re_f ≈ 2250.
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The Power of Test-Time Training for Approximate Sampling
Establishes a quadratic lower bound on query complexity for sampling from large classes of distributions given approximate density oracles, answers an open question on optimality of random walks, and shows circumvention for bounded classes as an abstraction of TTT.
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Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training
OGAS uses a parallel diffusion model to bias PDE configuration sampling toward high surrogate difficulty, reducing 99th-percentile errors and error variance versus uniform sampling across tested 2D PDEs.
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Continuous Language Diffusion as a Decoder-Interface Problem
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Where the Score Lives: A Wavelet View of Diffusion
Derives optimal score functions for diffusion models as wavelet expansions in terms of data moments, enabling architecture-agnostic analysis of which distribution attributes matter for denoising.
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FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views
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HumanFlow -- Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control
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Rao-Blackwellized Score Matching on Manifolds
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FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models
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Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
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ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos
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MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation
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Anchored Cyclic Generation: A Novel Paradigm for Long-Sequence Symbolic Music Generation
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Unlocking Prompt Infilling Capability for Diffusion Language Models
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GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow
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Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation
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The Diffusion-Attention Connection
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Latent Generative Solvers for Generalizable Long-Term Physics Simulation
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Contour Refinement using Discrete Diffusion in Low Data Regime
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DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching
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Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation
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Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
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Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
A LoRA-adapted conditional diffusion surrogate for electromagnetic calorimeter showers matches key observables within 2% RMSE and reproduces directional trends in design-utility gradients.
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HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control
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Chronos: A Physics-Informed Full-History Framework for Non-Markovian Long-Horizon Manipulation
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Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
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Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
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