Quantum convolutional HLA immunogenic peptide prediction (Q-CHIPP): Next-generation neoantigen prediction with quantum neural network

The rapid growth of quantum computing is driven by promises of performing complex calculations with unprecedented speed; however, current use cases have been limited by quantum hardware and the difficulty of identifying problems that classical computers cannot easily address. Within these constraints, biological problems including drug discovery, protein folding, and precision medicine present an opportunity to understand how current quantum hardware can make advances. In immunology, accurate prediction of cancer neoantigens remains a major challenge, limited by small, noisy datasets and the inability of classical models to generalize. In approaching the problem, we explore multiple noise mitigation techniques, including Pauli twirling and dynamical decoupling, in conjunction with controlled shot-based sampling to stabilize training on real hardware and in a warm start hybrid approach. With these approaches, we demonstrate the use of Quantum Convolutional Neural Networks (QCNNs) for both MHC binding and immunogenicity prediction, including a quantum hardware experiment involving 46 qubits that achieved a 6% increase in classification accuracy with fewer training samples compared to classical approaches. Building on these models, we introduce Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP), a combinatorial framework integrating MHC binding and T-cell recognition. It targets HLA-A*02:01-restricted 9-mer peptides and, more accurately, identifies those peptides known to be immunogenic, improving the prognostic impact of predicted neoantigen load. Together, these represent a large-scale application of QCNNs in biomedical modeling, highlighting both the feasibility and promise of quantum machine learning for data-limited biological systems and establishing a scalable foundation for quantum-enhanced biomedical research.