Scaling Quantum Machine Learning without Tricks: High-Resolution and Diverse Image Generation

ICLR 2026 Conference Submission16441 Authors

Keywords: quantum generative models, image generation, quantum machine learning, generative adversarial networks, quantum computing

TL;DR: We present scalable quantum generative adversarial networks that generate diverse full-resolution images with a single task-specific quantum circuit and multimodal noise, without the use of tricks such as dimensionality reduction or patch generation.

Abstract:

Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.
Contemporary quantum machine learning is generally limited to toy examples or heavily restricted datasets with few elements. This is not only due to the current limitations of available quantum hardware but also due to the absence of inductive biases arising from application-agnostic designs.
Current quantum solutions must resort to _tricks_ to scale down high-resolution images, such as relying heavily on dimensionality reduction or utilizing multiple quantum models for low-resolution image patches.
Building on recent developments in classical image loading to quantum computers, we circumvent these limitations and train quantum Wasserstein GANs on the established classical MNIST and Fashion-MNIST datasets. Using the complete datasets, our system generates full-resolution images across all ten classes and establishes a new state-of-the-art performance with a single end-to-end quantum generator without tricks.
As a proof-of-principle, we also demonstrate that our approach can be extended to color images, exemplified on the Street View House Numbers dataset.
We analyze how the choice of variational circuit architecture introduces inductive biases, which crucially unlock this performance. Furthermore, enhanced noise input techniques enable highly diverse image generation while maintaining quality. Finally, we show promising results even under quantum shot noise conditions.

Primary Area: generative models

Submission Number: 16441