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Quantum Machine Intelligence

Generative reconstruction for irrecoverable erasure errors in quantum-corrupted images

Prokash Chandra Roy, Saikat Barua, Md. Razwanul Islam Tanvir, Freyana Zanain, M. R. C. Mahdy

Published Nov 10, 2025• 0 citations
Publisher
DOI
10.1007/s42484-025-00329-6

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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.
Li and Boyle (2024) demonstrated that aperiodic tilings — including Penrose,Ammann-Beenker (AB), and Fibonacci geometries — give rise to a remarkable class ofquantum error-correcting codes (QECCs) in which quantum information is encoded throughthe geometric structure of the tiling itself. Their analysis establishes that these codes arerobust against erasure errors, where a finite patch of the tiling is deleted and reconstructedfrom the surrounding geometry. Here we introduce a substrate-level diagnostic for a distinctperturbation class we term silent relational corruption: the scrambling of a graph'sconnectivity topology while preserving local degree statistics. Unlike erasure, silent relationalcorruption produces no detectable gap in the substrate — the perturbation leaves no visibletrace. Although motivated by aperiodic quantum error-correcting codes, the present audit isclassical: it evaluates recoverability of privileged-persistence sites under controlled graphperturbations, not the performance of a quantum code. Using a matched degree-preservingrewiring protocol applied to AB (N=273) and Penrose (N=7,160) substrates generated viacut-and-project methods, we demonstrate that vulnerability to silent relational corruption issubstrate-dependent and predictable from a geometric invariant: the exo/endo persistenceaxis. AB substrates, whose structural logic is stored in hidden perpendicular-spacecoordinates (exo-addressed), retain near-ceiling identity recoverability under full relationalscrambling (AUC = 1.000). Penrose substrates, whose structural logic resides entirely in thelocal relational weave (endo-reconstructive), show substantially reduced identityrecoverability (AUC = 0.800). We further demonstrate that identity persistence and functionalreconstruction are operationally distinct processes — the former is address-led, the latterweave-led — and that hybrid architectures incorporating both channels providecomplementary redundancy under mixed damage. These findings suggest that silent relationalcorruption resistance should be evaluated as a substrate selection criterion for aperiodicerror-correcting architectures. Contact: niedzkatie@gmail.com
In optical imaging, various techniques have been studied to mitigate unwanted light. Mode-selective detection, which filters out unwanted light modes, is widely used to reduce the impact of external light. However, this technique cannot completely reject external light when it shares the same mode as the desired light. In this paper, we analyze an image reconstruction method, called mode-error-based imaging (MEI), inspired by a recent study on quantum-secured imaging, to reject image corruption from external environments, including noise. MEI constructs a target image using an estimated reconstruction factor obtained by analyzing the source light mode and the measured light mode. We simulate the capacity and provide a proof-of-principle demonstration of MEI. We investigated the conditions for enhancing image quality under external illumination with MEI compared to the mode-selective method. We anticipate that existing post-processing techniques for noise reduction, such as machine learning, can also be used in parallel to improve image quality. The noise reduction capability of MEI can be utilized not only in imaging systems with strong background noise, such as microwave-based imaging, but also in imaging systems with weak probes, such as bioimaging.