Enhancing Quantum Diffusion Models for Complex Image Generation

• URL: http://arxiv.org/abs/2602.03405v2
• Date: Thu, 05 Feb 2026 16:55:22 GMT
• Title: Enhancing Quantum Diffusion Models for Complex Image Generation
• Authors: Jeongbin Jo, Santanam Wishal, Shah Md Khalil Ullah, Shan Zeng, Dikshant Dulal,
• Abstract summary: In this study, we explore a Hybrid Quantum-Classical U-Net architecture integrated with Adaptive Non-Local Observables (ANO)<n>By compressing classical data into a dense quantum latent space and utilizing trainable observables, our model aims to extract non-local features that complement classical processing.<n> Experimental results on the full MNIST dataset (digits 0-9) demonstrate that the proposed architecture is capable of generating structurally coherent and recognizable images for all digit classes.
• Score: 0.8373057326694194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum generative models offer a novel approach to exploring high-dimensional Hilbert spaces but face significant challenges in scalability and expressibility when applied to multi-modal distributions. In this study, we explore a Hybrid Quantum-Classical U-Net architecture integrated with Adaptive Non-Local Observables (ANO) as a potential solution to these hurdles. By compressing classical data into a dense quantum latent space and utilizing trainable observables, our model aims to extract non-local features that complement classical processing. We also investigate the role of Skip Connections in preserving semantic information during the reverse diffusion process. Experimental results on the full MNIST dataset (digits 0-9) demonstrate that the proposed architecture is capable of generating structurally coherent and recognizable images for all digit classes. While hardware constraints still impose limitations on resolution, our findings suggest that hybrid architectures with adaptive measurements provide a feasible pathway for mitigating mode collapse and enhancing generative capabilities in the NISQ era.

Related papers

• Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence [63.39968536637762]
  We introduce Quantum LEGO Learning, a learning framework that treats classical and quantum components as reusable, composable learning blocks.<n>Within this framework, a pre-trained classical neural network serves as a frozen feature block, while a VQC acts as a trainable adaptive module.<n>We develop a block-wise generalization theory that decomposes learning error into approximation and estimation components.
  arXiv  Detail & Related papers  (2026-01-29T14:29:21Z)
• Enriching Earth Observation labeled data with Quantum Conditioned Diffusion Models [38.62950622229361]
  We introduce the Quanvolutional Conditioned U-Net (QCU-Net), a hybrid quantum--classical architecture that applies quantum operations within a conditioned diffusion framework.<n>Experiments on the EuroSAT RGB dataset demonstrate that our QCU-Net achieves superior results.<n>This work represents the first successful adaptation of class-conditioned quantum diffusion modeling in the Earth Observation domain.
  arXiv  Detail & Related papers  (2025-12-23T15:40:31Z)
• Hardware-inspired Continuous Variables Quantum Optical Neural Networks [0.27998963147546146]
  In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes.<n>This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks.
  arXiv  Detail & Related papers  (2025-12-04T19:20:27Z)
• Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions [47.08625631041616]
  Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy.<n>Existing QFL frameworks largely focus on homogeneity among quantum textcolorblackclients, and they do not account for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity.<n>These differences can create instability during training, slow convergence, and reduce overall model performance.
  arXiv  Detail & Related papers  (2025-11-27T06:35:45Z)
• Quantum Visual Fields with Neural Amplitude Encoding [70.86293548779774]
  We introduce a new type of Quantum Implicit Neural Representation (QINR) for 2D image and 3D geometric field learning.<n>QVF encodes classical data into quantum statevectors using neural amplitude encoding grounded in a learnable energy manifold.<n>Our ansatz follows a fully entangled design of learnable parametrised quantum circuits, with quantum (unitary) operations performed in the real Hilbert space.
  arXiv  Detail & Related papers  (2025-08-14T17:59:52Z)
• LCQNN: Linear Combination of Quantum Neural Networks [7.010027035873597]
  We introduce the Linear Combination of Quantum Neural Networks (LCQNN) framework, which uses the linear combination of unitaries concept to create a tunable design.<n>We show how specific structural choices, such as adopting $k$ of control unitaries or restricting the model to certain group-theoretic subspaces, prevent gradients from collapsing.<n>In group action scenarios, we show that by exploiting symmetry and excluding exponentially large irreducible subspaces, the model circumvents barren plateaus.
  arXiv  Detail & Related papers  (2025-07-03T17:43:10Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
  Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• Latent Style-based Quantum GAN for high-quality Image Generation [28.3231031892146]
  We introduce the Latent Style-based Quantum GAN (LaSt-QGAN), which employs a hybrid classical-quantum approach in training Generative Adversarial Networks (GANs) Our LaSt-QGAN can be successfully trained on realistic computer vision datasets beyond the standard MNIST, namely Fashion MNIST (fashion products) and SAT4 (Earth Observation images) with 10 qubits.
  arXiv  Detail & Related papers  (2024-06-04T18:00:00Z)
• Exploring quantum localization with machine learning [39.58317527488534]
  We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recognition capabilities of a modified convolutional model.
  arXiv  Detail & Related papers  (2024-06-01T08:50:26Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)

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.