Publications

This paper presents a systematic ablation and evaluation integrity study of hybrid quantum-classical neural networks for seven-class skin lesion classification on the HAM10000 dermoscopy dataset. We study the effect of the evaluation protocol on a hybrid quantum-classical pipeline by comparing two partitioning schemes on the same model and data. The first is a leakage-prone scheme that partitions the pool of augmented images, and the second is a verified scheme that partitions original lesion identities. Under the leakage-prone scheme, augmented variants of the same physical lesion appear on both sides of the split. An audit found that 1,095 of 4,810 lesions (22.8%) crossed the split boundary, artificially inflating all previously reported metrics. We rebuild the pipeline with a verified lesion-level partition consisting of 4,152 training, 522 validation, and 517 original test images. The training set is augmented to 10,503 images while ensuring zero image-level and lesion-level overlap. We then re-run every experiment across five random seeds with per-class Wilson confidence intervals and paired significance tests on a fixed held-out test set that is never used for tuning. Under the corrected protocol, the strongest configuration features a ResNet50 backbone with concatenation fusion, focal loss, and test-time augmentation. This setup reaches 78.41% ± 0.74 test accuracy and a macro F1 of 0.804 against a verified baseline of 76.17% ± 1.91 . However, under the leakage-prone protocol, this exact same pipeline yields 94.57%. A parameter-matched classical control, in which a 36-parameter multilayer perceptron replaces the variational quantum circuit (VQC), is statistically indistinguishable from the hybrid model (78.14% versus 78.41%, p = 0.81). Additionally, a controlled fusion comparison shows that concatenation is not significantly better than weighted fusion. We therefore report no detectable advantage from the quantum branch at this scale as a controlled negative result rather than a failure, and we characterize VQC trainability under noiseless and depolarizing simulation. Finally, a split-audit survey of recent quantum-hybrid HAM10000 studies indicates that reported accuracies above 90% are associated with undisclosed or leakage-prone evaluation protocols. The contribution of this work is methodological: a reproducible leakage audit, a verified correction protocol, an honest multi-seed baseline, a parameter-matched classical control, and evidence that evaluation protocols rather than architectures drive the headline differences reported in this literature.

This paper presents an enhanced quantum computing approach for DNA sequence alignment, integrating advanced noise management and robust error detection. We augment existing quantum alignment methodologies by incorporating supplementary qubits for each base, enabling improved alignment in noisy quantum environments. Our method models quantum circuits for matching reference and read DNA sequences using a hardware-calibrated noise model, specifically FakeJakartaV2, which incorporates depolarizing and thermal relaxation error characteristics. A key enhancement involves the integration of Zero-Noise Extrapolation (ZNE) for active error mitigation, with ZNE confidence serving as an early stopping criterion to enhance computational efficiency. Our error detection mechanism complements ZNE, providing an additional layer of verification for alignment integrity even when ZNE confidence is high. Experimental results demonstrate that this combined strategy effectively addresses sequence discrepancies, enhances error resilience, and produces more reliable alignment outcomes in quantum-based DNA alignment tasks compared to classical techniques. We validate our approach using IBM Quantum simulators, showcasing its potential for advancing large-scale genomic data analysis, addressing challenges of noise and errors in quantum DNA sequence alignment, and offering practical value through actionable error mitigation.