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arXiv Statistics ML By Chenyan Jia, Cong Guo, Siyue Chen, Pengpeng Ye, Xiaochun Chen

Improving Sample Efficiency in Peptide-HLA Binding Prediction with Hybrid Quantum-Classical Neural Networks

Read the original on arXiv Statistics ML →

The paper introduces a hybrid quantum-classical neural network (HQNN) for predicting peptide-HLA binding, a key step in personalized cancer immunotherapy. HQNN combines biological feature encoding, quantum feature extractors, and a quantum-enhanced classifier, outperforming a classical CNN baseline on two HLA alleles across all training sizes, especially when data are scarce. Ablation studies and noise-aware simulations confirm the benefits of the quantum components and the robustness of the approach under realistic hardware noise.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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