2025/07/15 by Emmanuel Billias, Billias, Emmanuel, Nikos Chrisochoides +1
Engineering · Medicine · Neuroscience · #Advanced X-ray and CT Imaging #Brain Tumor Detection and Classification #FOS: Physical sciences #Quantum Physics (quant-ph) #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.2507.10939
openalex publication_date 2025/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a two-level decomposition strategy to enhance the quality and performance of Quantum Hadamard Edge Detection (QHED) for practical image analysis on Noisy Intermediate-Scale Quantum (NISQ) devices. A Data-Level Decomposition partitions an input image into P augmented sub-images, each encoded into a separate quantum circuit. Each of these circuits is then further cut via Circuit-Level Decomposition into Q smaller sub-circuits suitable for execution on near-term quantum devices. The two-level P × Q decomposition, along with optimizations we introduced, achieves over 62% reductions in circuit depth and approximately 93% fewer two-qubit operations, while maintaining a fidelity exceeding 95.6% under realistic IBM noise models for 5-qubit data input sizes. These results demonstrate the feasibility of performing high-fidelity QHED on NISQ hardware and provide lessons and early evidence of distributed utility scale quantum computing, further illustrated by processing raw k-space MRI data with an Inverse Quantum Fourier Transform and a distributed simulation of the modified QHED on large 2D and 3D MRI datasets.