This work presents a novel quantum-hybrid pipeline for lung nodule classification in computed tomography (CT) scans, combining vision transformer (ViT) embeddings with quantum orthogonal pyramidal circuits (QOPCs). The approach was evaluated on 681 lung nodule CT scans across axial, coronal, and sagittal planes. Two ViT configurations were tested: ViT1 (1 head, 4 layers) and a Bayesian-optimized ViT2 (4 heads, 8 layers). Features extracted from ViT embedding layers were reduced via principal component analysis to 2–16 dimensions and classified using the QOPC with reconfigurable beam splitter (RBS) gates. The proposed approach achieved unprecedented compression, up to 1,470<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\times $</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT2, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$k{=}8$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>k</mi> <mo>=</mo> <mn>8</mn> </mrow> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$h{=}8$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>h</mi> <mo>=</mo> <mn>8</mn> </mrow> </math></EquationSource> </InlineEquation>) with only 46 trainable parameters and achieved computational efficiency (CE) up to 92.0. Training was accelerated up to 28<InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\times $</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> (0.030 vs. 0.833 min) while maintaining robust diagnostic performance (F1: 0.77−0.82, receiver operating characteristic area under the curve (ROC-AUC): 0.87−0.90). Ablation studies confirmed that the quantum layer outperforms conventional MLPs by +3.4% accuracy with 35% fewer parameters, while late fusion of multi-view predictions further improved performance to 85.4% accuracy and 0.92 ROC-AUC. These results establish hybrid ViT-QOPC architectures as a practical and resource-efficient framework for medical image analysis, demonstrating their ability to dramatically reduce computational cost without compromising clinical accuracy.

Vision transformer embeddings and quantum pyramidal circuits for biomedical image analysis

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2026年5月25日

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Xavi F. Aragones,
Miguel A. González Ballester

摘要

This work presents a novel quantum-hybrid pipeline for lung nodule classification in computed tomography (CT) scans, combining vision transformer (ViT) embeddings with quantum orthogonal pyramidal circuits (QOPCs). The approach was evaluated on 681 lung nodule CT scans across axial, coronal, and sagittal planes. Two ViT configurations were tested: ViT₁ (1 head, 4 layers) and a Bayesian-optimized ViT₂ (4 heads, 8 layers). Features extracted from ViT embedding layers were reduced via principal component analysis to 2–16 dimensions and classified using the QOPC with reconfigurable beam splitter (RBS) gates. The proposed approach achieved unprecedented compression, up to 1,470 $\times $ $\times$ (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT₂, $k{=}8$ $k = 8$ , $h{=}8$ $h = 8$ ) with only 46 trainable parameters and achieved computational efficiency (CE) up to 92.0. Training was accelerated up to 28 $\times $ $\times$ (0.030 vs. 0.833 min) while maintaining robust diagnostic performance (F1: 0.77−0.82, receiver operating characteristic area under the curve (ROC-AUC): 0.87−0.90). Ablation studies confirmed that the quantum layer outperforms conventional MLPs by +3.4% accuracy with 35% fewer parameters, while late fusion of multi-view predictions further improved performance to 85.4% accuracy and 0.92 ROC-AUC. These results establish hybrid ViT-QOPC architectures as a practical and resource-efficient framework for medical image analysis, demonstrating their ability to dramatically reduce computational cost without compromising clinical accuracy.