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Quantum Kernel-Based Cancer Atlas: Hardware-Validated Mapping of Gene Expression to Drug Response

Gulfam Hussain

Published Apr 17, 2026• 0 citations
Publisher

Abstract

This work presents a hardware-validated quantum framework for encoding cancer gene expression data and evaluating its alignment with drug response signals. Using a quantum kernel-based architecture (Qube Engine), classical gene expression data from CCLE is transformed into quantum states and executed on IBM Quantum hardware. Outputs are compared against IC50 drug response values from the GDSC dataset. The system demonstrates strong stability under real hardware noise (~97.15%) and maintains measurable biological relevance, achieving a correlation of ~0.45 with IC50 values. This work highlights the feasibility of applying quantum machine learning techniques to real-world biomedical datasets under NISQ constraints.

DOI
10.5281/zenodo.19633727

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This work presents a hardware-validated quantum framework for encoding cancer gene expression data and evaluating its alignment with drug response signals. Using a quantum kernel-based architecture (Qube Engine), classical gene expression data from CCLE is transformed into quantum states and executed on IBM Quantum hardware. Outputs are compared against IC50 drug response values from the GDSC dataset. The system demonstrates strong stability under real hardware noise (~97.15%) and maintains measurable biological relevance, achieving a correlation of ~0.45 with IC50 values. This work highlights the feasibility of applying quantum machine learning techniques to real-world biomedical datasets under NISQ constraints.
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
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Quantum machine learning (QML) is considered one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in medicine and biology fields is still in its infancy with few examples. In this study, we aim to assess whether quantum kernels (QK) could effectively classify subtypes of breast cancer (BC) patients based on molecular characteristics. We performed a heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the quantum processing unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on NISQ devices. Our findings suggest that QK methods show promises for application in precision oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.
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 IC 50 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 ⟨ Z 0 ⟩. 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.
The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, to take the computational advantage of quantum machine learning, researchers have to deal with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to improve quantum machine learning methods and provide useful insights into how they can help solve complex classification problems in different fields.
Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the tanh⁡\tanhtanh. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers.