Integrating the various omics to conduct a thorough research of biological systems is known as multi-omics. It enables a comprehensive comprehension of the intricate relationships and dynamics that exist within an organism. Understanding complicated biological systems and developing bioengineering applications like disease modelling, metabolism engineering, & precision medicine depend on integrating of multi-omics data. However, traditional computational methods are severely hampered by the high complexity, heterogeneity, & nonlinear interactions among genomes, genomics, proteomics, and metabolomics data. In order to effectively describe intricate cross-omics interactions, this research proposes a quantum computing-based framework for multiple-omics data integration that makes use of the concepts of quantum juxtaposition and entanglement. A hybrid cognitive–classical architecture is suggested, wherein classical optimisation methods are used for training and multi-omics features are converted into states of matter and processed utilising variational quantum circuits. Phenotype prediction and route analysis are two downstream bioengineering tasks that make use of the integrated quantum representations. In comparison to classical approaches, the suggested strategy delivers better integration efficiency and prediction performance, as demonstrated by experimental assessments utilising simulated quantum settings. The findings demonstrate quantum computing's promise as a potent instrument for precise and scalable multi-omics data integration, opening the door for applications in systems biology and bioengineering of the future.
Integrating the various omics to conduct a thorough research of biological systems is known as multi-omics. It enables a comprehensive comprehension of the intricate relationships and dynamics that exist within an organism. Understanding complicated biological systems and developing bioengineering applications like disease modelling, metabolism engineering, & precision medicine depend on integrating of multi-omics data. However, traditional computational methods are severely hampered by the high complexity, heterogeneity, & nonlinear interactions among genomes, genomics, proteomics, and metabolomics data. In order to effectively describe intricate cross-omics interactions, this research proposes a quantum computing-based framework for multiple-omics data integration that makes use of the concepts of quantum juxtaposition and entanglement. A hybrid cognitive–classical architecture is suggested, wherein classical optimisation methods are used for training and multi-omics features are converted into states of matter and processed utilising variational quantum circuits. Phenotype prediction and route analysis are two downstream bioengineering tasks that make use of the integrated quantum representations. In comparison to classical approaches, the suggested strategy delivers better integration efficiency and prediction performance, as demonstrated by experimental assessments utilising simulated quantum settings. The findings demonstrate quantum computing's promise as a potent instrument for precise and scalable multi-omics data integration, opening the door for applications in systems biology and bioengineering of the future.
Integrating the various omics to conduct a thorough research of biological systems is known as multi-omics. It enables a comprehensive comprehension of the intricate relationships and dynamics that exist within an organism. Understanding complicated biological systems and developing bioengineering applications like disease modelling, metabolism engineering, & precision medicine depend on integrating of multi-omics data. However, traditional computational methods are severely hampered by the high complexity, heterogeneity, & nonlinear interactions among genomes, genomics, proteomics, and metabolomics data. In order to effectively describe intricate cross-omics interactions, this research proposes a quantum computing-based framework for multiple-omics data integration that makes use of the concepts of quantum juxtaposition and entanglement. A hybrid cognitive–classical architecture is suggested, wherein classical optimisation methods are used for training and multi-omics features are converted into states of matter and processed utilising variational quantum circuits. Phenotype prediction and route analysis are two downstream bioengineering tasks that make use of the integrated quantum representations. In comparison to classical approaches, the suggested strategy delivers better integration efficiency and prediction performance, as demonstrated by experimental assessments utilising simulated quantum settings. The findings demonstrate quantum computing's promise as a potent instrument for precise and scalable multi-omics data integration, opening the door for applications in systems biology and bioengineering of the future.
Multi-omics integration has become a cornerstone of personalized medicine, enabling researchers to connect genomic, transcriptomic, proteomic, metabolomic, and epigenomic data to reveal complex biological mechanisms and individualized disease signatures. However, the extreme dimensionality, heterogeneity, and nonlinear interactions across omics layers challenge even the most advanced classical machine learning methods. Quantum Machine Learning (QML) offers a powerful alternative by exploiting quantum superposition, entanglement, and high-dimensional feature embeddings to model multi-omics interactions more efficiently. This study proposes a hybrid quantum–classical framework that combines quantum kernel estimation, variational quantum classifiers, and quantum-enhanced feature fusion networks to generate integrative biological signatures for precision diagnostics. Simulation results using benchmark multi-omics datasets demonstrate improved classification accuracy, enhanced sensitivity to weak biological signals, and reduced computational overhead compared to classical baselines. These findings highlight the potential of QML to accelerate multi-omics insights and pave the way toward truly personalized, predictive healthcare.
Precision medicine aims to tailor healthcare by integrating individual genetic, epigenetic, transcriptomic, proteomic, and clinical data, collectively referred to as multi-omic data. However, the scale and complexity of such multi-omics datasets challenge classical computing approaches. Quantum computing, which leverages superposition and entanglement (quantum-level correlations between particles), offers a fundamentally new paradigm for accelerating molecular simulations, biomarker discovery, and high-dimensional data analysis. This review explores the convergence of quantum computing and it's potential to provide unmet needs in precision biomedicine research, with emphasis on applications in diagnostic modeling, multi-omic data integration and drug discovery. We highlight early proof-of-concept studies demonstrating the use of quantum machine learning for disease prediction, quantum algorithms for protein folding, and quantum generative models for novel drug design. Hybrid quantum-classical workflows are also already enabling gene network inference and prioritization of variants of uncertain significance, the latter of which is a major focus of multi-omic research worldwide. Emerging directions include digital twin simulations and real-time clinical decision support powered by quantum models. Looking ahead, the long-term vision for quantum computing in biomedicine involves in silico modeling of entire biological systems to simulate cellular responses to perturbations like drug treatments, enabling clinicians to test therapies in virtual patients before real-world application. Despite these advances, practical implementation remains limited by hardware constraints, qubit decoherence, algorithm scalability, and regulatory barriers. Nonetheless, as quantum hardware evolves and AI-aligned quantum algorithms mature, their integration holds transformative potential. Quantum computing may eventually shorten diagnostic timelines, improve therapeutic precision, and make biomedical innovation more globally accessible. We outline a roadmap for translating these technologies into next-generation precision medicine.
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
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.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning’s role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug-receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual's genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions. This paper provides a comprehensive analysis of the potential, challenges, and future directions of quantum-AI synergy in transforming modern healthcare.