Towards a Utility-Scale Quantum Edge Detection for Real-World Medical Image Data

• URL: http://arxiv.org/abs/2507.10939v1
• Date: Tue, 15 Jul 2025 03:03:33 GMT
• Title: Towards a Utility-Scale Quantum Edge Detection for Real-World Medical Image Data
• Abstract summary: We present a two-level decomposition strategy to enhance the quality and performance of Quantum Hadamard Edge Detection (QHED)<n>A Data-Level Decomposition partitions an input image into P augmented sub-images, each encoded into a separate quantum circuit.<n>A Circuit-Level Decomposition is used to cut each of these circuits into Q smaller sub-circuits suitable for execution on near-term quantum devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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