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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.10939 |
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Table of Contents:
- 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 $\times$ 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.