Accelerated Magnetic Resonance Imaging via Quantum Computing and Deep Neural Network

Motivation: Applying quantum computing to the reconstruction of magnetic resonance imaging. Goal(s): Improving the quality of magnetic resonance reconstruction images by introducing quantum computing. Approach: Building a quantum hybrid classical neural network for undersampling magnetic resonance reconstruction. Results: The hybrid classical neural network achieved better accuracy by introducing quantum convolutional layers and verified the feasibility of applying quantum computing to magnetic resonance reconstruction. Impact: This fully demonstrates the potential and broad development prospects of quantum computing in accelerating magnetic resonance imaging.