mlsanityData to 7 Oct 2026

Quantum Diffusion Models for Few-Shot Learning

arXiv 2411.04217 ↗First posted 6 Nov 2024cs.LG cs.AIcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets. Most state-of-the-art QML algorithms currently lack practical advantages due to their limited learning capabilities, especially in few-shot learning tasks. In this work, we propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning: label-guided generation inference (LGGI); label-guided denoising inference (LGDI); and label-guided noise addition inference (LGNAI). Experimental results demonstrate that our proposed algorithms significantly outperform existing methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 16 of its 29 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

16 of 29 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Quantum Denoising Diffusion Models13 Jan
  2. 2023Quantum-Noise-Driven Generative Diffusion Models23 Aug
  3. 2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning31 Jul
  4. 2020Denoising Diffusion Implicit Models6 Oct
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 11

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  3. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  4. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 301
  5. 2021Generalization in quantum machine learning from few training datacited by 214
  6. 2023Text-to-Image Diffusion Models are Zero-Shot Classifierscited by 140
  7. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  8. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  9. 2021QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalizationcited by 27
  10. 2022AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communicationscited by 6
  11. 2024JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networkscited by 2

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  1. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
  2. 2023Generative quantum machine learning via denoising diffusion probabilistic models
  3. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Model
  4. 2023Quantum circuit synthesis with diffusion models
  5. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generation
  6. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
  7. 2024Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
  8. 2025Synthesis of discrete-continuous quantum circuits with multimodal diffusion models