Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell
Muralikrishnan Gopalakrishnan Meena, Dileep Kishore, Jerry M. Parks, Luke Bertels, Dilipkumar N. Asthagiri, Travis Humble, Thomas L. Beck, Mitchel J. Doktycz
quant-phphysics.bio-phSubmitted Jul 30, 2026• Announced Jul 31, 2026• 0 citations
Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to $μ$m) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.
Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to μm) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.
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.
This paper introduces an algorithmic quantum simulation framework designed to efficiently model and understand complex systems, particularly those exhibiting emergent properties. The core aim is to leverage the strengths of both quantum and classical computation to accelerate simulation and analysis of these systems. We propose a hybrid approach that utilizes quantum algorithms for key computational bottlenecks, alongside classical optimization and exploration techniques to achieve a significant speedup over traditional simulation methods. This work centers on a specific example—modeling the dynamics of biological networks – and demonstrates the potential for this hybrid strategy to unlock new insights into complex phenomena. The paper concludes with a discussion of the challenges and future directions of this research.
Quantum computing is emerging as a transformative approach in bioinformatics and quantum chemistry, addressing fundamental computational limitations of classical methods in molecular science. Many biological and chemical systems are inherently governed by quantum mechanics, making accurate simulation with classical algorithms computationally expensive or infeasible. This review examines how quantum computing, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, is advancing molecular modeling, reaction dynamics, and bioinformatics applications. Key quantum algorithms, including the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), demonstrate increasing capability in approximating molecular energies and simulating quantum systems. In parallel, applications in bioinformatics—such as protein folding, genomic search, and combinatorial optimization—highlight the potential of quantum computing to address complex biological problems. Quantum machine learning further extends these capabilities by integrating quantum algorithms with data-driven modeling. However, current progress is constrained by NISQ-era limitations, including noise, limited qubit counts, and scalability challenges. These constraints highlight the need for hybrid quantum–classical approaches and improved error mitigation strategies. Overall, quantum computing represents a complementary computational paradigm that can enhance classical methods in bioinformatics and chemistry. Continued advances in quantum algorithms, hardware, and hybrid modeling frameworks are expected to expand its role in molecular science and computational biology.
Multiscale modeling of complex chemical systems requires algorithms that operate coherently across electronic, atomistic, mesoscopic, and continuum scales. While quantum algorithms have been proposed for each regime, no systematic framework exists to compose them across scale boundaries. Here, we identify the conditions under which fault-tolerant quantum algorithms might preserve scale-specific quantum advantages. We map quantum phase estimation, Hamiltonian simulation with Gibbs state preparation, quantum random walks, and quantum partial differential equation solvers onto electronic structure, molecular dynamics, mesoscopic kinetics, and continuum reactor physics, respectively. Crucially, these correspondences do not imply unconditional end-to-end quantum advantage; speedups depend heavily on state preparation, memory architectures, matrix conditioning, and classical readout costs. Six unresolved questions define this composition problem, illustrated via a quantum hierarchy for \ce{CO} oxidation over \ce{Pt(111)}. We propose viewing inter-scale transfer as a quantum channel composition problem at the interface of algorithm design and non-equilibrium statistical mechanics, and ask whether information loss at scale boundaries is intrinsic to multiscale modeling or merely a consequence of lossy classical transduction between algorithmic layers. The resulting roadmap suggests that multiscale quantum advantage is governed primarily by the structure of information transfer between algorithmic layers, rather than by performance at individual scales alone.
Quantum computing has emerged as a potential catalyst for addressing computational bottlenecks that increasingly constrain biotechnologies, including drug discovery, structural biology, and precision medicine. As biological datasets grow in scale and complexity, classical high-performance computing struggles to efficiently model molecular interactions and high-dimensional biological systems. Recent advances in hybrid quantum-classical algorithms and quantum-enhanced machine learning have enabled early proof-of-concept applications on noisy intermediate-scale quantum (NISQ) devices, offering new strategies for molecular simulation, pattern recognition, and optimization. In this opinion article, we critically assess where quantum computing may deliver realistic near-term value for biomedicine, emphasize hybrid workflows as the most viable path forward, and outline how quantum technologies could complement, not replace, existing computational paradigms as the field progresses toward fault-tolerant systems.
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.