Advancing genomics and integration of multi-omics for precision oncology using quantum machine learning.

Authors

Ji-Yong Sung, Jae-Ho Cheong

Published in

NPJ digital medicine. Jul 11, 2026. Epub Jul 11, 2026.

Abstract

Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offers a new paradigm using superposition, entanglement, and quantum interference to efficiently explore vast solution spaces. This Perspective highlights how quantum algorithms-such as Quantum Support Vector Machines, Quantum Principal Component Analysis, and quantum generative models-could enhance key tasks in precision oncology, including multi-omics integration, spatial transcriptomics, and neoantigen prediction. Current technical barriers, like qubit noise and limited quantum memory, are discussed alongside strategies to connect quantum computing with biomedical research. Interdisciplinary collaboration will be essential to realizing quantum advantage in cancer multi-omics.

PMID:
42436320
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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