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.
We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp quantum-software framework (v 0.8.2). Each encoding was evaluated on n = 50 stratified samples drawn from the Genomics of Drug Sensitivity in Cancer dataset (GDSC2, 242 036 drug–cell-line pairs), targeting the natural-log IC50 response variable. Variational weights were optimised offline with the gradient-free COBYLA algorithm before hardware submission. Every circuit was executed with 1024 shots; the regression signal is the zero-qubit Pauli expectation value ⟨Z0⟩. Results show that the QAOA-inspired encoding achieves the best RMSE of 3.314 and is statistically superior (p < 0.05, Wilcoxon signed-rank test) to six of the remaining eleven encodings. Hardware-efficient entanglement structures—specifically alternating cost and mixer layers—provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions. This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data; all code, data, and raw QPU outputs are publicly released.
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
Intrinsically Disordered Regions (IDRs) play essential roles in cellular processes through interactions with proteins, nucleic acids, lipids, and metal ions, yet predicting their binding partners remains challenging for understanding protein function and drug discovery. However, current computational methods including protein language models face performance plateaus where traditional approaches to improve accuracy have become ineffective. Here, we present 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. Through systematic evaluation of quantum circuit architectures across factorial experiments, we demonstrate that the hybrid model achieves statistically significant performance improvements over classical baselines, with entanglement topology governing model stability and encoding methods determining performance gains. These findings establish that quantum advantage in computational biology emerges from architectural design principles rather than computational scale, providing a framework for overcoming performance limitations in bioinformatics applications where dataset expansion is constrained.
Seok-Jin Kang, Hongchul Shin· IEEE transactions on computa...· 0 citations
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML 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
Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offers a new paradigm using superposition, entanglement, and quantum interference to efficiently explore vast solution spaces. 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. Current technical barriers, like qubit noise and limited quantum memory, are discussed alongside strategies to connect quantum computing with biomedical research. Interdisciplinary collaboration will be essential to realizing quantum advantage in cancer multi-omics.
Ji-Yong Sung, Jae-Ho Cheong· npj Digital Medicine· 0 citations