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International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT)

Quantum-Assisted Drug Discovery for Lung Cancer

Kaushalya Thopate, Nandini Naik, Ayushi Patil, Aaradhya Pawar, Tanvi Pokale

Published Apr 1, 2026• 0 citations
Publisher
DOI
10.1109/tqcebt67648.2026.11681175

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Quantum machine learning (QML) is a rising research field that is an intersection of two major domains -quantum computing and machine learning (ML). In recent times, it has been employed to solve complex problems in domains such as image processing, healthcare, drug discovery, natural language processing and finance. The advantages of QML algorithms over traditional ML models include reduced computation resources and the ability to handle and solve complex problems as well as multiple parameters. In this study, we employ QML models for lung cancer prediction in the smart healthcare system domain. We adopted QML approaches, i.e., VQC and Pegasos QSVC, to improve the detection efficiency of lung cancer. We used different performance parameters, such as training accuracy and statistical measures, to evaluate the QML models and also performed a comparative study with the existing baseline models. We analyse that the Pegasos QSVC model outperforms other QML models with a classification accuracy of 85%.
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms of non-small cell lung cancer. Our methodology involves a two-phase process: in Phase 1, differential expression analysis and methylation analysis between tumor and normal samples allows us to identify LUAD-specific and LUSC-specific genes, revealing potential prognostic biomarkers for cancer subtypes. Phase 2 focuses on developing a quantum classifier capable of distinguishing between LUAD and LUSC tumors, as well as between tumor and normal samples. This classifier not only enhances diagnostic precision but also demonstrates the quantum advantage in processing large-scale multiomic datasets. Our results consistently demonstrated that Sample3, representing the combined gene set, achieved the highest overall predictive performance in all metrics. These results demonstrate that QML provides an effective and scalable approach for biomarker discovery and subtype specific cancer classification. GO enrichment analysis highlighted the significant involvement of genes in synaptic signaling, ion channel regulation, and neuronal development. In the quantum phase, KEGG analysis further identified enrichment in cancer-associated pathways, including neurotrophin, MAPK, Ras, and PI3KAkt signaling, with key genes such as NGFR, NTRK2, and NTF3 suggesting a central role in neurotrophinmediated oncogenic processes. Our findings highlight the growing potential of quantum computing to advance precision oncology and next-generation biomedical analytics.