Quantum computing algorithms: getting closer to critical problems in computational biology
Laura Marchetti, Riccardo Nifosı̀, Pier Luigi Martelli, Eleonora Da Pozzo, Valentina Cappello, Francesco Banterle, Maria Letizia Trincavelli, Claudia Martini, Massimo D’Elia
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.
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.
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.
Abstract Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
The explosive growth of genomic information calls for computational methods to handle complex sequence analysis and genome assembly assays. Based on the principles of superposition and entanglement, quantum computing represents an another way of computation to achieve potentially faster and more efficient operations than classical systems. This article offers a comparative overview of 4 major studies dedicated to the use of QC in genomics, spanning theoretical algorithmical conceptualization, systematic literature mapping, analyses on practical challenges in QC-aided genomics and hybrid Q-C designs. The review indicates that there is potential for significant speed-up based on theoretical formulation, while current applications are limited by hardware resource constraint, data-loading latency and scalability. Hybrid models, in particular those that hybridize quantum annealing with classical optimization, seem to offer the most practicable route to near-term genomic computing. The analysis also shines a light on the increasingly pivotal positions that quantum-inspired approaches and hybrid orchestration systems are playing as crucial stepping stones towards full-quantum advantage. The results suggest that quantum computing is in its infancy as far as genomic analysis is concerned but the potential seems to be promising for future bioinformatics research and applications. Such opportunities are only accessible with further advancements in quantum hardware, data encoding and benchmark development.
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.
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.