Enriching Earth Observation labeled data with Quantum Conditioned Diffusion Models

• URL: http://arxiv.org/abs/2512.20448v1
• Date: Tue, 23 Dec 2025 15:40:31 GMT
• Title: Enriching Earth Observation labeled data with Quantum Conditioned Diffusion Models
• Authors: Francesco Mauro, Francesca De Falco, Lorenzo Papa, Andrea Ceschini, Alessandro Sebastianelli, Paolo Gamba, Massimo Panella, Silvia Ullo,
• Abstract summary: 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.
• Score: 38.62950622229361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid adoption of diffusion models (DMs) in the Earth Observation (EO) domain has unlocked new generative capabilities aimed at producing new samples, whose statistical properties closely match real imagery, for tasks such as synthesizing missing data, augmenting scarce labeled datasets, and improving image reconstruction. This is particularly relevant in EO, where labeled data are often costly to obtain and limited in availability. However, classical DMs still face significant computational limitations, requiring hundreds to thousands of inference steps, as well as difficulties in capturing the intricate spatial and spectral correlations characteristic of EO data. Recent research in Quantum Machine Learning (QML), including initial attempts of Quantum Generative Models, offers a fundamentally different approach to overcome these challenges. Motivated by these considerations, we introduce the Quanvolutional Conditioned U-Net (QCU-Net), a hybrid quantum--classical architecture that applies quantum operations within a conditioned diffusion framework using a novel quanvolutional feature-extraction approach, for generating synthetic labeled EO imagery. Extensive experiments on the EuroSAT RGB dataset demonstrate that our QCU-Net achieves superior results. Notably, it reduces the Fréchet Inception Distance by 64%, lowers the Kernel Inception Distance by 76%, and yields higher semantic accuracy. Ablation studies further reveal that strategically positioning quantum layers and employing entangling variational circuits enhance model performance and convergence. This work represents the first successful adaptation of class-conditioned quantum diffusion modeling in the EO domain, paving the way for quantum-enhanced remote sensing imagery synthesis.

Related papers

• Enhancing Quantum Diffusion Models for Complex Image Generation [0.8373057326694194]
  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.
  arXiv  Detail & Related papers  (2026-02-03T11:32:19Z)
• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation [83.22330172077308]
  AiDE-Q iteratively generates high-quality synthetic labeled datasets.<n>We conduct extensive numerical simulations on a diverse set of quantum many-body and molecular systems.
  arXiv  Detail & Related papers  (2025-09-30T11:29:14Z)
• 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)
• Quantum-Accelerated Neural Imputation with Large Language Models (LLMs) [0.0]
  This paper introduces Quantum-UnIMP, a novel framework that integrates shallow quantum circuits into an LLM-based imputation architecture.<n>Our experiments on benchmark mixed-type datasets demonstrate that Quantum-UnIMP reduces imputation error by up to 15.2% for numerical features (RMSE) and improves classification accuracy by 8.7% for categorical features (F1-Score) compared to state-of-the-art classical and LLM-based methods.
  arXiv  Detail & Related papers  (2025-07-11T02:00:06Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models.<n>This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space.<n>The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement [35.436358464279785]
  This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning.
  arXiv  Detail & Related papers  (2024-11-24T20:14:57Z)
• Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks [62.12107686529827]
  This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5% in EO use cases.
  arXiv  Detail & Related papers  (2024-07-24T09:11:34Z)
• Quantum Vision Transformers for Quark-Gluon Classification [3.350407101925898]
  We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits. We evaluate our method by applying the model to multi-detector jet images from CMS Open Data.
  arXiv  Detail & Related papers  (2024-05-16T17:45:54Z)

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.