Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement

TL;DR

This paper presents a quantum diffusion model that leverages pairwise Bell-state entanglement to efficiently process complex images on NISQ devices, improving performance and resource utilization over previous methods.

Contribution

It introduces a novel pairwise Bell-state entangling technique combined with parameterized circuits for efficient quantum image generation on limited hardware.

Findings

Significant improvements in FID, SSIM, and PSNR metrics.

Enhanced computational efficiency compared to classical and existing quantum methods.

Effective processing of high-dimensional images on qubit-limited NISQ devices.

Abstract

This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. Unlike previous methods, this model efficiently processes higher-dimensional images with complex pixel structures, even on qubit-limited platforms. This is accomplished through a pairwise Bell-state entangling technique, which reduces space complexity. Additionally, parameterized quantum circuits enable the generation of quantum states with minimal parameters, while still delivering high performance. We conduct comprehensive experiments, comparing the proposed model with both classical and quantum techniques using datasets such as MNIST and CIFAR-10. The results show significant improvements in computational efficiency and performance metrics such as FID, SSIM and PSNR. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning. This advancement paves the way for more sophisticated and resource-efficient quantum diffusion algorithms capable of handling complex data on the NISQ devices.