Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2510.12628 · Physical Review A

Quantum network-based prediction of cancer driver genes

Patrícia Marques, Andreas Wichert, Duarte Magano, Bruno Coelho Coutinho

Published Apr 20, 2026• 1 citation
arXivHTMLPDF

Abstract

Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein-interaction (PPI) networks offers a promising avenue for their detection [Horn , ; Nourbakhsh , ], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger [], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a state-preparation scheme we call quantum multiorder moment embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors and encodes this information into quantum states. These states are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer-gene prediction. This work underscores the potential of supervised quantum-graph-learning frameworks to advance biological discovery.

DOI
10.1103/lrw9-cvbh

Related papers

Quantum Interaction Manifolds for Cancer Genomics: A Theoretical Framework and Proof-of-Concept Quantum Machine Learning for Early Diagnosis and Drug Response Modeling

Parham Ghayour

Abstract Understanding and predicting cancer phenotypes from genomic data requires models capableof capturing high-order interactions among genes and biological pathways. We introduce Quantum Interaction Manifolds (QIMs), a theoretical framework that represents gene expression profiles as quantumstates evolved under biologically informed Hamiltonians. We show that QIM embeddings induce hypothesisclasses that cannot be efficiently approximated by classical learners under standard complexity assumptions,providing a principled quantum advantage in representing nonlinear genomic dependencies. Using noisysimulations on reduced-dimensional datasets, we demonstrate that QIM-based quantum kernels achievecompetitive accuracy in cancer classification tasks, while variational quantum models trained on QIMrepresentations learn continuous drug response signals with stable convergence. Together, these results establish QIMs as a quantum-native approach for modeling genomic interactions and highlight the potentialof structured quantum machine learning to support future developments in computational oncology

•Dec 31, 2025•0 citations

MQML: Multi-Omic Quantum Machine Learning Based Cancer Classification, Biomarker Identification in Human Lung Adenocarcinoma

Mandeep Kaur Saggi, Sabre Kais

Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182, MIR520a, PYGO2, EMX2OS, EMX2, RP11-550F7.1, RPS11, MIR20a, EIF4G2, IGLV1-47, RP11-180M15.7, SCYL3, AC107081.5, C2orf47, FST, MIR7-1, SLC22A18, CLDN5, and AC007879.7 identified as having significant and non-significant p-values asso-ciated with LUAD patient predictions.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•5 citations

Predicting Drug Sensitivity in TP53-Mutated Cancer via Quantum Boltzman Machines

Vasile Vancea

Mutations in the TP53 gene are among the most prevalent alterations in human cancers and are known to influence therapeutic responses. Accurate prediction of drug sensitivity based on TP53 mutation profiles remains a major challenge in precision oncology. In this study, we propose a quantum-enhanced framework for genomic classification using Quantum Boltzmann Machines (QBMs) applied to quantum-encoded DNA sequences. Exon 5–8 fragments of TP53 containing both wild-type and clinically relevant mutations were encoded using angle-based quantum rotations and input into a QBM architecture trained to predict binary drug response (sensitive vs resistant) based on half-maximal inhibitory concentration (IC50) thresholds. Simulated results demonstrate that the QBM achieved over $\mathbf{9 1 \%}$ accuracy in classifying drug sensitivity, outperforming classical Boltzmann models and revealing interpretable latent features. This work showcases the potential of QBMs in modeling sequence-driven phenotypes and supports their application in biomarker discovery, drug response prediction, and quantum-enabled precision medicine.

IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)•Oct 16, 2025•0 citations

Hybrid Quantum-Classical Graph Neural Networks for Tumor Classification in Digital Pathology

Anupama Ray, Dhiraj Madan, Srushti Patil +8 more

Advances in classical machine learning and single-cell technologies have paved the way for understanding interactions between disease cells and tumor microenvironments towards accelerating therapeutic discovery. However, challenges in these machine learning methods and NP-hard problems in spatial biology create an opportunity to explore quantum computing algorithms. Here, we present a hybrid quantum-classical graph neural network (GNN) that combines a classical GNN with a type of quantum neural network (QNN) called a variational quantum classifier (VQC) for classifying binary sub-tasks in breast cancer subtyping. We explore two variants of the framework, the first with fixed pretrained GNN parameters and the second with end-to-end training of GNN+VQC. The results demonstrate that the hybrid quantum-classical neural network is at par with the state-of-the-art classical graph neural networks in terms of weighted precision, recall, and F1-score at higher dimensions. We also show that by means of amplitude encoding, we can compress information in logarithmic number of qubits and attain better performance in comparison to classical compression (which leads to information loss), while keeping the number of qubits required constant in both regimes. Finally, we show some results that end-to-end training enables an improvement over fixed GNN parameters and also slightly improves over vanilla GNN with the same number of dimensions.

arXiv:2310.11353•IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•14 citations

Quantum-Enhanced Neural Networks for Cancer Detection Using Gene Expression Data

Suneel Kumar Komali, Persis Voola, Santhikumari Geddada

Accurate discrimination between benign and malignant tumours from molecular data is one of the most consequential tasks in clinical oncology, yet gene-expression profiles are extremely high-dimensional and challenge conventional classifiers with overfitting and heavy parameter budgets. This paper presents OncoVQC, a hybrid quantum-classical framework for cancer-malignancy classification from integrated gene-expression and clinical features. A disciplined classical pipeline of mutual-information feature selection, principal component analysis, and angle normalisation reduces fifty-eight raw features to six encoding angles, which are processed by a six-qubit Variational Quantum Classifier (VQC) employing genuine data re-uploading and a hardware-efficient entangling ansatz across four layers. The model is trained with the gradient-free COBYLA optimiser against a binary cross-entropy objective and is delivered behind a FastAPI prediction service with a React dashboard for real-time inference. On a balanced multi-cancer cohort of 5,200 samples spanning four tumour types, OncoVQC achieves 88.5% test accuracy and a ROC-AUC of 0.960 using only forty-eight trainable parameters two to four orders of magnitude fewer than competitive classical ensembles while an ablation study confirms that data re-uploading and entanglement are the decisive contributors to its performance.

•Oct 15, 2026•0 citations

Classifying Hotspots Mutations for Biosimulation with Quantum Neural Networks and Variational Quantum Eigensolver

Don Roosan, Rubayat Islam Khan, Saif Nirzhor +2 more

The rapid expansion of biomolecular datasets presents significant challenges for computational biology. Quantum computing emerges as a promising solution to address these complexities. This study introduces a novel quantum framework for analyzing TART-T and TART-C gene data by integrating genomic and structural information. Leveraging a Quantum Neural Network (QNN), we classify hotspot mutations, utilizing quantum superposition to uncover intricate relationships within the data. Additionally, a Variational Quantum Eigensolver (VQE) is employed to estimate molecular ground-state energies through a hybrid classical-quantum approach, overcoming the limitations of traditional computational methods. Implemented using IBM Qiskit, our framework demonstrates high accuracy in both mutation classification and energy estimation on current Noisy Intermediate-Scale Quantum (NISQ) devices. These results underscore the potential of quantum computing to advance the understanding of gene function and protein structure. Furthermore, this research serves as a foundational blueprint for extending quantum computational methods to other genes and biological systems, highlighting their synergy with classical approaches and paving the way for breakthroughs in drug discovery and personalized medicine.

arXiv:2507.00072•International Conference on Data Science, Technology and Applications (DATA)•Jan 1, 2025•14 citations
Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein-interaction (PPI) networks offers a promising avenue for their detection [Horn , ; Nourbakhsh , ], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger [], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a state-preparation scheme we call quantum multiorder moment embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors and encodes this information into quantum states. These states are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer-gene prediction. This work underscores the potential of supervised quantum-graph-learning frameworks to advance biological discovery.
Abstract Understanding and predicting cancer phenotypes from genomic data requires models capableof capturing high-order interactions among genes and biological pathways. We introduce Quantum Interaction Manifolds (QIMs), a theoretical framework that represents gene expression profiles as quantumstates evolved under biologically informed Hamiltonians. We show that QIM embeddings induce hypothesisclasses that cannot be efficiently approximated by classical learners under standard complexity assumptions,providing a principled quantum advantage in representing nonlinear genomic dependencies. Using noisysimulations on reduced-dimensional datasets, we demonstrate that QIM-based quantum kernels achievecompetitive accuracy in cancer classification tasks, while variational quantum models trained on QIMrepresentations learn continuous drug response signals with stable convergence. Together, these results establish QIMs as a quantum-native approach for modeling genomic interactions and highlight the potentialof structured quantum machine learning to support future developments in computational oncology
Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182, MIR520a, PYGO2, EMX2OS, EMX2, RP11-550F7.1, RPS11, MIR20a, EIF4G2, IGLV1-47, RP11-180M15.7, SCYL3, AC107081.5, C2orf47, FST, MIR7-1, SLC22A18, CLDN5, and AC007879.7 identified as having significant and non-significant p-values asso-ciated with LUAD patient predictions.
Mutations in the TP53 gene are among the most prevalent alterations in human cancers and are known to influence therapeutic responses. Accurate prediction of drug sensitivity based on TP53 mutation profiles remains a major challenge in precision oncology. In this study, we propose a quantum-enhanced framework for genomic classification using Quantum Boltzmann Machines (QBMs) applied to quantum-encoded DNA sequences. Exon 5–8 fragments of TP53 containing both wild-type and clinically relevant mutations were encoded using angle-based quantum rotations and input into a QBM architecture trained to predict binary drug response (sensitive vs resistant) based on half-maximal inhibitory concentration (IC50) thresholds. Simulated results demonstrate that the QBM achieved over 91%\mathbf{9 1 \%}91% accuracy in classifying drug sensitivity, outperforming classical Boltzmann models and revealing interpretable latent features. This work showcases the potential of QBMs in modeling sequence-driven phenotypes and supports their application in biomarker discovery, drug response prediction, and quantum-enabled precision medicine.
Advances in classical machine learning and single-cell technologies have paved the way for understanding interactions between disease cells and tumor microenvironments towards accelerating therapeutic discovery. However, challenges in these machine learning methods and NP-hard problems in spatial biology create an opportunity to explore quantum computing algorithms. Here, we present a hybrid quantum-classical graph neural network (GNN) that combines a classical GNN with a type of quantum neural network (QNN) called a variational quantum classifier (VQC) for classifying binary sub-tasks in breast cancer subtyping. We explore two variants of the framework, the first with fixed pretrained GNN parameters and the second with end-to-end training of GNN+VQC. The results demonstrate that the hybrid quantum-classical neural network is at par with the state-of-the-art classical graph neural networks in terms of weighted precision, recall, and F1-score at higher dimensions. We also show that by means of amplitude encoding, we can compress information in logarithmic number of qubits and attain better performance in comparison to classical compression (which leads to information loss), while keeping the number of qubits required constant in both regimes. Finally, we show some results that end-to-end training enables an improvement over fixed GNN parameters and also slightly improves over vanilla GNN with the same number of dimensions.
Accurate discrimination between benign and malignant tumours from molecular data is one of the most consequential tasks in clinical oncology, yet gene-expression profiles are extremely high-dimensional and challenge conventional classifiers with overfitting and heavy parameter budgets. This paper presents OncoVQC, a hybrid quantum-classical framework for cancer-malignancy classification from integrated gene-expression and clinical features. A disciplined classical pipeline of mutual-information feature selection, principal component analysis, and angle normalisation reduces fifty-eight raw features to six encoding angles, which are processed by a six-qubit Variational Quantum Classifier (VQC) employing genuine data re-uploading and a hardware-efficient entangling ansatz across four layers. The model is trained with the gradient-free COBYLA optimiser against a binary cross-entropy objective and is delivered behind a FastAPI prediction service with a React dashboard for real-time inference. On a balanced multi-cancer cohort of 5,200 samples spanning four tumour types, OncoVQC achieves 88.5% test accuracy and a ROC-AUC of 0.960 using only forty-eight trainable parameters two to four orders of magnitude fewer than competitive classical ensembles while an ablation study confirms that data re-uploading and entanglement are the decisive contributors to its performance.
The rapid expansion of biomolecular datasets presents significant challenges for computational biology. Quantum computing emerges as a promising solution to address these complexities. This study introduces a novel quantum framework for analyzing TART-T and TART-C gene data by integrating genomic and structural information. Leveraging a Quantum Neural Network (QNN), we classify hotspot mutations, utilizing quantum superposition to uncover intricate relationships within the data. Additionally, a Variational Quantum Eigensolver (VQE) is employed to estimate molecular ground-state energies through a hybrid classical-quantum approach, overcoming the limitations of traditional computational methods. Implemented using IBM Qiskit, our framework demonstrates high accuracy in both mutation classification and energy estimation on current Noisy Intermediate-Scale Quantum (NISQ) devices. These results underscore the potential of quantum computing to advance the understanding of gene function and protein structure. Furthermore, this research serves as a foundational blueprint for extending quantum computational methods to other genes and biological systems, highlighting their synergy with classical approaches and paving the way for breakthroughs in drug discovery and personalized medicine.