Ryan Peters, Kahn Rhrissorrakrai, Prerana Bangalore Parthasarathy, Vadim Ratner, Tanvi P. Gujarati, Meltem Tolunay, Jie Shi, Jeffrey K. Weber, Timothy An-thy Chan, Laxmi Parida, Sara Capponi, Filippo Utro, Tyler Joseph Alban
ABSTRACT The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal cells such as cancer. A key player in the immunologic recognition of non-self is the immune synapse, where T cell receptors (TCRs) scan peptides presented on major histocompatibility complex (MHC) molecules to detect and eliminate cells displaying non-self antigens. While this interaction is vital for vaccine and immunotherapy success, the underlying rules of TCR recognition remain poorly understood. This is only further challenged as the application of predictive models is very limited due to small training datasets. While traditional machine learning models excel at predicting neoantigen binding to MHC, they often struggle to accurately predict immunogenicity. To address these challenges, we developed a quantum computing approach using Quantum Convolutional Neural Networks (QCNNs). Here we present Quantum Convolutional Human Leukocyte Antigen (HLA) Immunogenic Peptide Prediction (Q-CHIPP), the first application of quantum hardware based on training/predicting both MHC binding and immunogenicity in a combinatorial approach. Additionally, we present a large scale use of quantum hardware at scale with 46 qubits. This study underscores how quantum technology can be used for biological modeling and presents a scalable QCNN design with the potential to overcome current computational bottlenecks as quantum hardware advances.
ABSTRACT The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal cells such as cancer. A key player in the immunologic recognition of non-self is the immune synapse, where T cell receptors (TCRs) scan peptides presented on major histocompatibility complex (MHC) molecules to detect and eliminate cells displaying non-self antigens. While this interaction is vital for vaccine and immunotherapy success, the underlying rules of TCR recognition remain poorly understood. This is only further challenged as the application of predictive models is very limited due to small training datasets. While traditional machine learning models excel at predicting neoantigen binding to MHC, they often struggle to accurately predict immunogenicity. To address these challenges, we developed a quantum computing approach using Quantum Convolutional Neural Networks (QCNNs). Here we present Quantum Convolutional Human Leukocyte Antigen (HLA) Immunogenic Peptide Prediction (Q-CHIPP), the first application of quantum hardware based on training/predicting both MHC binding and immunogenicity in a combinatorial approach. Additionally, we present a large scale use of quantum hardware at scale with 46 qubits. This study underscores how quantum technology can be used for biological modeling and presents a scalable QCNN design with the potential to overcome current computational bottlenecks as quantum hardware advances.
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.
Peptide-HLA binding prediction is a critical step in neoantigen identification for personalized cancer immunotherapy and holds significant clinical value. However, the training data available for many HLA alleles are extremely limited, which severely constrains the performance of conventional methods on this task. Parameterized quantum circuits are hypothesized to induce inductive biases beneficial for learning from small datasets, yet their application to biological sequence prediction remains underexplored. To address this, we propose a hybrid quantum-classical neural network (HQNN) specifically designed for peptide-HLA binding prediction. HQNN integrates multi-source biological feature encoding with parallel quantum feature extractors and a quantum-enhanced classifier. On two HLA alleles (A*02:01 and B*07:02), HQNN outperforms a parameter-matched classical CNN baseline across all training sizes, with the performance gap widening as training data decreases. Ablation studies confirm the respective contributions of the quantum feature extraction module and the quantum classifier. In noise-aware simulations, performance degrades only mildly, and such degradation is reasonable and acceptable under realistic quantum hardware noise levels. These results suggest that hybrid quantum-classical architectures can provide practical sample-efficiency gains for immunoinformatics tasks in low-data regimes.
One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible to identify the most promising candidates from a large pool of potential compounds, reducing the number of compounds that need to be tested experimentally. Recently, deep learning methods have shown superior performance than traditional computational methods for making accurate predictions on large datasets. However, the complexity and time-consuming nature of these methods have limited their usage and development. Quantum machine learning is an emerging technology that has the potential to improve many classical machine learning algorithms. In this work we present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical network while maintaining optimal performance in the predictions. Additionally, it results in a significant time savings of up to 40% in the training process, which means a meaningful speed up of the drug discovery process.
Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. Accordingly, precise binding affinity prediction, enabling the accurate selection of suitable candidates from an extensive pool of potential compounds, can greatly reduce the expenses associated to practical experimental protocols. In this respect, recent advances revealed that deep learning methods show superior performance compared to other traditional computational methods, especially with the advent of large datasets. These methods, however, are complex and very time-intensive, thus representing an important clear bottleneck for their development and practical application. In this context, the emerging realm of quantum machine learning holds promise for enhancing numerous classical machine learning algorithms. In this work, we take one step forward and present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical counterpart while still maintaining optimal performance in the predictions. Additionally, this results in a significant cost and time savings of up to 40% in the training stage, which means a substantial speed-up of the drug design process.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.
Proteins are linear polymers that fold into an incredible variety of three-dimensional structures that enable sophisticated functionality for biology. Predicting protein distance with high precision remains challenging, particularly for small protein families. As deep learning achieves remarkable success in many areas, deep learning also allows scientists to predict proteins' three-dimensional structure. As convolutional neural networks have a powerful ability to learn data features at multiple levels of abstraction, we deploy CNN to predict protein distance. To accelerate the training process, we apply a quantum convolutional neural network(QCNN) to improve the protein structure prediction efficiently. For QCNN, the conventional convolutional layer is transformed to a quantum convolution or quanvolutional layer. Since the protein data has large input, we explore the large dimension of these quantum transformations. And in experiments, we compare the different number of layers in QCNN during the training phase. We found the QCNN is similar to CNN that is the deeper layer can get better performance. The simulations show the proposed method can accelerate the convergence while maintaining the performance.