This work introduces Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP), a combinatorial framework integrating MHC binding and T-cell recognition that targets HLA-A*02:01–restricted 9-mer peptides and identifies those peptides known to be immunogenic, improving the prognostic impact of predicted neoantigen load.
Abstract
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.
This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data and shows that hardware-efficient entanglement structures—specifically alternating cost and mixer layers—provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions.
Rania Derouich, N. Mathlouthi· bioRxiv· 0 citations
Deep generative models have become a leading approach for designing therapeutic molecules, yet efficiently exploring vast biomolecular sequence spaces remains difficult, particularly for targets with limited training data. The prior distribution that seeds a generative model shapes which regions of sequence space it explores, and recent work suggests that non-classical distributions sampled from quantum processors can serve as a structured alternative to the factorised Gaussian priors used by default. Whether such priors help on complex biological design tasks has been largely untested. Here we present what is, to our knowledge, the first end-to-end hybrid quantum-classical pipeline for de novo design of MHC class I-binding peptides, coupling a generative adversarial network (GAN) to latent vectors sampled from a real photonic quantum processor. Tested in silico across 131 HLA alleles, quantum-derived priors increased the yield of predicted strong binders, with the largest relative gains for understudied alleles where classical baselines perform worst. We selected three understudied alleles for further evaluation, finding that large gains coincided with broader sequence exploration at non-anchor positions while anchor specificity was preserved. On these three alleles, we validated the designs in vitro using peptide-MHC stability ELISAs, confirming that quantum-designed peptides are potent stabilisers of peptide-MHC class I complexes. These results establish structured, hardware-realisable non-classical priors as a useful inductive bias for generative peptide design, with direct relevance to personalised immunotherapies and vaccines.
E. S. Engdal, Jonathan Funk, O. Bacarreza et al.· bioRxiv· 0 citations
A hybrid quantum-classical machine learning approach that combines variational quantum circuits with the ESM2 protein language model for multi-class IDR binding partner prediction using a prototypical network is presented, establishing that quantum advantage in computational biology emerges from architectural design principles rather than computational scale.
Seok-Jin Kang, Hongchul Shin· IEEE transactions on computa...· 0 citations
A structured perspective on the current state of QML is provided to identify opportunities in deploy it effectively in solve real-world problems and to identify opportunities in deploy it effectively in solve real-world problems.
Muhamad Akrom, Aprilyani Nur Safitri, N. Hidayat et al.· Journal of Multiscale Materi...· 0 citations
Generative models for de novo molecular design have become a cornerstone of computational drug discovery, yet existing hybrid quantum-classical approaches remain confined to small-molecule benchmarks such as QM9 ($\boldsymbol{\leq} \mathbf{9}$ heavy atoms). This work introduces BO-QGAN+, a Bayesian-optimised hybrid quantum generative adversarial network that, for the first time, demonstrates competitive molecular generation on the ZINC-250k benchmark, accommodating molecules of up to 38 heavy atoms. Our primary methodological novelty lies in a multicircuit quantum ensemble generator featuring data reuploading variational circuits, which enables scaling to the substantially larger molecular graphs present in ZINC-250k. This generator is coupled with a Wasserstein critic stabilised by gradient penalty (WGAN-GP) and a graph-convolutional discriminator with spectral normalisation. On a 5,000-molecule ZINC-250k subset, BO-QGAN+ achieves the best Drug Candidate Score (DCS) of 1.244 with 94% molecular validity, a QED of 0.41, normalised logP of 0.71, and normalised SA of 0.43 within 700 training epochs These results are obtained with only 16 quantum parameters alongside 2.03 M classical parameters, demonstrating a parameter efficiency ratio exceeding 127,000:1. At epoch 700, the model further achieves Uniqueness of 0.97, Novelty of 0.84, and Internal Diversity of 0.79, confirming that high validity does not come at the expense of generation diversity. Our findings establish that hybrid quantum generators can operate effectively at molecular scales well beyond prior quantum GAN benchmarks, Key limitations include the use of classical quantum simulation rather than physical NISQ hardware, and evaluation on a 5,000-molecule subset. Nevertheless, our findings open a practical pathway toward quantum-assisted drug discovery pipelines on near-term quantum hardware.
Ouail El Bani, Chaïmaâ Kissi, M. N. Srifi· IEEE International Conferenc...· 0 citations
This Perspective highlights how quantum algorithms-such as Quantum Support Vector Machines, Quantum Principal Component Analysis, and quantum generative models-could enhance key tasks in precision oncology, including multi-omics integration, spatial transcriptomics, and neoantigen prediction.
Ji-Yong Sung, Jae-Ho Cheong· npj Digital Medicine· 0 citations
