TL;DR
The results establish that encoding choice determines not only classification performance but also trainability, with optimal strategies differing between kernel and variational settings.
The findings across the five datasets indicate that the choice of quantum feature encoding strategy should account for the underlying structure of the data and that predictive performance should be evaluated together with quantum circuit complexity.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed,...
A key challenge in practical quantum machine learning (QML), particularly for discriminative tasks such as classification, is the limited capacity of near-term quantum devices to encode high-dimensional classical data into small quantum registers. In optimized basis-encoded (bit-bit) settings, this constraint leads to...
It is indicated that for small structured datasets, classifiers based on quantum computing have not yet surpassed well-tuned classical counterparts, though not on malignant-class recall, where the VQC in particular performed worse than the classical baselines.
The results demonstrate why controlled component attribution is necessary before crediting a hybrid model's performance to its quantum layer, and analyze bottleneck, simulation, finite-shot, and noise limitations.
Findings show that current quantum feature maps through the encode–measure–boost pipeline on NISQ hardware do not yet outperform a well-designed classical pipeline.
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