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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/2504.20654 |
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| _version_ | 1866909596939976704 |
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| author | Lee, Hyunju Jun, Kyungtaek |
| author_facet | Lee, Hyunju Jun, Kyungtaek |
| contents | We propose a quantum-assisted reconstruction framework for high-resolution tomographic imaging that significantly reduces both qubit requirements and radiation exposure. Conventional quantum reconstruction methods require solving QUBO (Quadratic Unconstrained Binary Optimization) problems over full-resolution image grids, which limits scalability under current hardware constraints. Our method addresses this by combining sinogram downscaling with region-wise iterative refinement, allowing reconstruction to begin from a reduced-resolution sinogram and image, then progressively upscaled and optimized region by region. Experimental validation on binary and integer-valued Shepp-Logan phantoms demonstrates accurate reconstructions under both dense and sparsely sampled projection conditions using significantly fewer qubits. We observed that nearest-neighbor interpolation may cause edge artifacts that hinder convergence, which can be mitigated by smoother interpolation and Gaussian filtering. Notably, reconstructing a 500 by 500 image from a 50 by 50 initialization demonstrates the potential for up to 90% reduction in projection data, corresponding to a similar reduction in radiation dose. These findings highlight the practicality and scalability of the proposed method for quantum-enhanced tomographic reconstruction, offering a promising direction for low-dose, high-fidelity imaging with current-generation quantum devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging Lee, Hyunju Jun, Kyungtaek Quantum Physics We propose a quantum-assisted reconstruction framework for high-resolution tomographic imaging that significantly reduces both qubit requirements and radiation exposure. Conventional quantum reconstruction methods require solving QUBO (Quadratic Unconstrained Binary Optimization) problems over full-resolution image grids, which limits scalability under current hardware constraints. Our method addresses this by combining sinogram downscaling with region-wise iterative refinement, allowing reconstruction to begin from a reduced-resolution sinogram and image, then progressively upscaled and optimized region by region. Experimental validation on binary and integer-valued Shepp-Logan phantoms demonstrates accurate reconstructions under both dense and sparsely sampled projection conditions using significantly fewer qubits. We observed that nearest-neighbor interpolation may cause edge artifacts that hinder convergence, which can be mitigated by smoother interpolation and Gaussian filtering. Notably, reconstructing a 500 by 500 image from a 50 by 50 initialization demonstrates the potential for up to 90% reduction in projection data, corresponding to a similar reduction in radiation dose. These findings highlight the practicality and scalability of the proposed method for quantum-enhanced tomographic reconstruction, offering a promising direction for low-dose, high-fidelity imaging with current-generation quantum devices. |
| title | Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.20654 |