Quantum-Inspired and Optimization-Based CT Reconstruction Through Spin-Error

Abstract

Quantum-optimization-based computed tomography (CT) reconstruction has demonstrated quantum supremacy in reconstruction accuracy over conventional classical methods under sparse-view and corrupted-sinogram conditions. However, scaling this approach remains challenging because larger images require increasingly many binary variables and interactions in the underlying quadratic unconstrained binary optimization (QUBO) model. Even with iterative region-of-interest (ROI) refinement, a 60 by 60 ROI with three X-ray mass attenuation coefficients requires 10,800 binary variables under our encoding. As an initial step toward larger quantum CT images, we investigate simulated annealing (SA) followed by spin-error mitigation for optimisation (SEMO), without altering the CT-QUBO objective. We evaluate three 54-view ROI reconstruction instances and nine-view reconstruction with detector-row corruption, comparing SA and SEMO with selected hybrid quantum-classical solver results. For the ROI cases, SEMO reduced the numbers of incorrectly reconstructed pixels in low-budget SA outputs from 13 to 1, 38 to 0, and 54 to 15. With 20 percent detector-row corruption, three of four selected SA and SEMO pairs achieved exact reconstruction. Applying SEMO to one hybrid-solver output reduced its ROI error from 78 to 1 pixel. Improvements were instance-dependent: in one corrupted-sinogram case, QUBO energy decreased while image-domain error increased slightly; in one ROI case, the refined image retained 15 incorrect pixels despite having lower QUBO energy than ground truth. At the evaluated problem sizes, SA and SEMO attained reconstruction accuracy comparable to or better than selected unmitigated hybrid-solver results, providing an initial solution-quality benchmark for future larger-scale, parallelized quantum CT image reconstruction.

This paper examines whether classical simulated annealing (SA), followed by spin-error mitigation for optimisation (SEMO), can improve CT reconstructions formulated as quadratic unconstrained binary optimisation (QUBO) problems. The motivation is scaling: under the stated encoding, a $60\times60$ region of interest (ROI) with three X-ray mass attenuation coefficients requires 10,800 binary variables. SEMO is applied as post-processing without changing the CT-QUBO objective. The reported evaluation covers three 54-view ROI reconstruction instances and nine-view reconstruction with detector-row corruption, with comparisons against selected hybrid quantum–classical solver outputs. For the three ROI instances, SEMO reduced incorrectly reconstructed pixels in low-budget SA outputs from 13 to 1, 38 to 0, and 54 to 15. Under 20% detector-row corruption, three of four selected SA–SEMO pairs achieved exact reconstruction. Post-processing one hybrid-solver output reduced its ROI error from 78 pixels to 1. At these evaluated sizes, SA with SEMO produced accuracy comparable to or better than the selected unmitigated hybrid-solver results. The abstract identifies instance-dependent performance and a mismatch between optimisation energy and reconstruction accuracy. In one corrupted-sinogram instance, lower QUBO energy accompanied slightly greater image error; in one ROI instance, 15 incorrect pixels remained despite an energy below that of the ground-truth image. The results are presented as an initial solution-quality benchmark, rather than a demonstration of larger-scale reconstruction or quantum advantage. Only the abstract is available, limiting assessment of implementation details, computational budgets, and comparison protocols.