TE-HQViT-Probs: Full-Probability Quantum Self-Attention Outperforms Expectation-Value Readout in Medical Image Classification.

<title>Abstract</title> <p>Hybrid quantum-classical neural networks commonly convert a parameterized quantum circuit state into classical features through expectation-value measurements. Although compact, this readout may discard useful information contained in the circuit state. This study investigates whether full computational-basis probability readout improves a compact hybrid quantum self-attention model for lightweight medical image classification. A token-embedded hybrid quantum vision transformer, TE-HQViT-Probs, was evaluated on PneumoniaMNIST, BreastMNIST, and OrganAMNIST. Query and key projections were computed classically, whereas the value path used amplitude embedding, a four-qubit, two-layer parameterized quantum circuit, and full-probability readout. Experiments used ten random seeds and compared full-probability readout with expectation-value readout, classical attention controls, a frozen-random probability-readout control, and standard classical vision baselines. Additional analyses included a controlled readout ladder, paired Wilcoxon tests, two one-sided tests (TOST) for practical equivalence, readout compression, frozen readout-only probes, and a mixed-state depolarizing-channel stress test. In the main benchmark, TE-HQViT-Probs improved mean area under the receiver operating characteristic curve (AUC) over the expectation-value variant by 0.0062, 0.0497, and 0.0236 on PneumoniaMNIST, BreastMNIST, and OrganAMNIST, respectively. In the controlled readout-ladder experiment, Full-Probs improved over Expval-Z by 0.0052, 0.0847, and 0.0266. The effects were nevertheless dataset-dependent: PneumoniaMNIST showed only a small improvement, BreastMNIST remained below a classical softmax-attention control, and frozen-random probability readout performed similarly to trainable probability readout in several settings. These results indicate that full-probability readout can preserve class-relevant information lost by simple expectation-value readout in small-qubit simulation experiments. The conclusion is methodological rather than computational: readout design is an important architectural factor in hybrid quantum attention models. The results do not establish quantum advantage, hardware speedup, or clinical deployment readiness.</p>