Quantum Neural Network for Accelerated Magnetic Resonance Imaging

TL;DR

This paper introduces a hybrid quantum-classical neural network designed to accelerate magnetic resonance image reconstruction from undersampled data, demonstrating promising results on quantum simulation systems.

Contribution

It proposes a novel hybrid quantum-classical neural network architecture for MRI reconstruction, showcasing its feasibility and effectiveness through simulation experiments.

Findings

Achieved high-quality MRI reconstruction results

Confirmed feasibility of hybrid quantum-classical approach

Demonstrated potential quantum advantages in imaging

Abstract

Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum computing has discovered that quantum convolution can improve network accuracy, possibly due to potential quantum advantages. This article proposes a hybrid neural network containing quantum and classical networks for fast magnetic resonance imaging, and conducts experiments on a quantum computer simulation system. The experimental results indicate that the hybrid network has achieved excellent reconstruction results, and also confirm the feasibility of applying hybrid quantum-classical neural networks into the image reconstruction of rapid magnetic resonance imaging.