Quantum AI for Cancer Diagnostic Biomarker Discovery

arXiv cs.LG / 4/22/2026

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Key Points

The paper proposes a quantum machine learning (QML) approach to support computational biology tasks in precision oncology, including cancer subtype classification and biomarker discovery.
Using a two-phase workflow, it first performs differential expression and methylation analyses to derive LUAD-specific and LUSC-specific candidate genes, then builds a quantum classifier to distinguish LUAD vs. LUSC and tumor vs. normal.
The study reports the best predictive performance when using Sample3, defined as a combined gene set, across all evaluation metrics.
Functional enrichment analyses (GO and KEGG) suggest that the identified genes are linked to pathways such as synaptic signaling, ion channel regulation, neurotrophin signaling, and major cancer-related signaling cascades.
The authors argue the results indicate a scalable QML strategy with “quantum advantage” for large-scale multi-omic datasets, pointing to future advances in biomedical diagnostics.

Abstract

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.