Introduction: This study aimed to evaluate the validity of the HandyGym portable flywheel device with an integrated rotary encoder in measuring force and power during iso-inertial exercises compared to a traditional reference system. Methods: In total, 10 trained volunteers (3 women, 7 men; age 25.2 ± 3.8 years) performed half-squats with five different load configurations using the HandyGym device. Concurrent measurements were obtained from HandyGym’s rotary encoder and a criterion system (MuscleLab 6000 strain gauge and linear encoder). Five load configurations were tested, with 15 repetitions recorded per condition. The validity of the HandyGym measurements was assessed through mean bias, typical error of estimation (TEE), and Pearson correlation coefficients, with Bland–Altman plots used to analyze the agreement between the two systems. Results: The HandyGym showed high correlations with the reference system for both force (r = 0.76–0.90) and power (r = 0.60–0.94). However, systematic biases were observed, with the HandyGym consistently underestimating force and power at lower loads and overestimating power at higher loads. The TEE values indicated moderate to large errors, particularly in power measurements. Conclusion: The HandyGym provides valid force measurements with moderate bias, suitable for general monitoring. However, power measurements are less consistent, especially at higher loads, limiting the device’s utility for precise assessments. Adjustments or corrections may be necessary for accurate application in professional contexts.

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: ViT ₁ (1 head, 4 layers) and a Bayesian-optimized ViT ₂ (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 $$\times $$ × (from 10,290 to 7 parameters) while preserving over 99% of baseline accuracy. The approach reached 83.7% accuracy (ViT ₂ , $$k{=}8$$ 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 $$\times $$ × (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.