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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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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Rao-Blackwellized Score Matching on Manifolds
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Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
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Denoising Diffusion Implicit Models
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DiffWave: A Versatile Diffusion Model for Audio Synthesis
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Diffusion-warm sampling of the XY model enables fast thermalization at scale
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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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Autoregressive Boltzmann Generators
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Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models
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Constrained Diffusion Models with Primal-Dual Inference
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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
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The Power of Test-Time Training for Approximate Sampling
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$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models
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Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
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Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns
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Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
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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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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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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
Early and late denoising steps in masked diffusion LMs are robust to smaller-model replacement, enabling 17% FLOPs reduction with modest generative quality loss.
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Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
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Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
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Scalable Multi Agent Diffusion Policies for Coverage Control
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Measurement-Based Quantum Diffusion Models
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