Advancing single-cell omics and cell-based therapeutics with quantum computing - Nature

AbstractThe generation of highly accurate models of behaviours of individual cells and cell populations through integration of high-resolution assays with advanced computational tools would transform precision medicine. Recent breakthroughs in single-cell and spatial transcriptomics and multi-omics technologies, coupled with artificial intelligence, are driving rapid progress in model development. Complementing the advances in artificial intelligence, quantum computing is maturing as a novel compute paradigm that may offer potential solutions to overcome the computational bottlenecks inherent to capturing cellular dynamics. In this Roadmap article, we discuss the advancements and challenges in spatiotemporal single-cell analysis, explore the possibility of quantum computing to address the challenges and present a case study on how quantum computing may be integrated into cell-based therapeutics. The specific confluence of quantum and classical computing with high-resolution assays may offer a crucial path towards the generation of transformative models of cellular behaviours and perturbation responses. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution Access options Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Learn more Buy this articlePurchase on SpringerLinkInstant access to the full article PDF.USD 39.95Prices may be subject to local taxes which are calculated during checkout Fig. 1: Overview of spatial and single-cell assays and relevant quantum computing techniques.Fig. 2: Map of computational tasks performed by classical and quantum computation algorithms for spatiotemporal single-cell analysis.Fig. 3: Overview of a quantum-enabled cell-based therapeutics. ReferencesMethod of the year 2013. Nat. Methods 11, 1 (2014).Marx, V. Method of the year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).Article CAS PubMed Google Scholar Method of the year 2024: spatial proteomics. Nat. Methods 21, 2195–2196 (2024).The Cancer Genome Atlas Research Network et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).Article Google Scholar de Bruijn, I. et al. Sharing data from the Human Tumor Atlas Network through standards, infrastructure and community engagement. Nat. Methods 22, 664–671 (2025).Article PubMed PubMed Central Google Scholar Jain, S. et al. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat. Cell Biol. 25, 1089–1100 (2023).Article CAS PubMed PubMed Central Google Scholar Rozenblatt-Rosen, O., Stubbington, M. J., Regev, A. & Teichmann, S. A.

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Bioinforma. 24, bbad040 (2023).Article Google Scholar Download referencesAuthor informationAuthors and AffiliationsIBM Research, Yorktown Heights, NY, USAAritra Bose, Kahn Rhrissorrakrai, Filippo Utro, Laxmi Parida, Aldo Guzman-Saenz, Joseph A. Morrone & Daniel PlattPurdue University, West Lafayette, IN, USASaugata BasuIBM Research, Zurich, SwitzerlandJannis BornIBM Research, San Jose, CA, USASara CapponiChildren’s Hospital of Philadelphia, Philadelphia, PA, USADimitra ChalkiaCenter for Immunotherapy and Precision-Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USATimothy A. ChanNational Center for Regenerative Medicine, Cleveland Clinic, Cleveland, OH, USATimothy A. ChanIBM Quantum, San Jose, CA, USAHakan Doga, Tanvi Gujarati, Gavin O. Jones, Meltem Tolunay & Jeannette M. GarciaQuantumBasel, uptownBasel Infinity Corp., Arlesheim, SwitzerlandFrederik F. FlötherBroad Institute of MIT and Harvard, Cambridge, MA, USAGad GetzMassachusetts General Hospital Cancer, Boston, MA, USAGad GetzDepartment of Pathology, Harvard Medical School, Boston, MA, USAGad GetzAlgorithmiq Ltd., Helsinki, FinlandMark Goldsmith, Stefan Knecht & Sabrina ManiscalcoAthos Therapeutics Inc, Los Angeles, CA, USADimitrios IliopoulosIBM Research, Gurugram, IndiaDhiraj MadanIBM Quantum, Dublin, IrelandNicola Mariella & Sergiy ZhukQ-Ctrl, Cambridge, MA, USAKhadijeh NajafiJohnson and Johnson, Zurich, SwitzerlandPushpak PatiUniversity of Lausanne, Lausanne, SwitzerlandMaria Anna RapsomanikiIBM Quantum, Bengaluru, IndiaAnupama RayIBM Quantum, Yorktown Heights, NY, USAOmar ShehabIBM Quantum, Zurich, SwitzerlandIvano Tavernelli & Stefan WoernerAuthorsAritra BoseView author publicationsSearch author on:PubMed Google ScholarKahn RhrissorrakraiView author publicationsSearch author on:PubMed Google ScholarFilippo UtroView author publicationsSearch author on:PubMed Google ScholarLaxmi ParidaView author publicationsSearch author on:PubMed Google ScholarConsortiathe Quantum for Healthcare Life Sciences ConsortiumSaugata Basu, Jannis Born, Aritra Bose, Sara Capponi, Dimitra Chalkia, Timothy A. Chan, Hakan Doga, Frederik F. Flöther, Gad Getz, Mark Goldsmith, Tanvi Gujarati, Aldo Guzman-Saenz, Dimitrios Iliopoulos, Gavin O. Jones, Stefan Knecht, Dhiraj Madan, Sabrina Maniscalco, Nicola Mariella, Joseph A. Morrone, Khadijeh Najafi, Pushpak Pati, Daniel Platt, Maria Anna Rapsomaniki, Anupama Ray, Kahn Rhrissorrakrai, Omar Shehab, Ivano Tavernelli, Meltem Tolunay, Filippo Utro, Stefan Woerner, Sergiy Zhuk, Jeannette M. Garcia & Laxmi ParidaContributionsThe authors contributed equally to all aspects of the article.Corresponding authorCorrespondence to Laxmi Parida.Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature Reviews Molecular Cell Biology thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.GlossaryAnsatz A parameterized quantum circuit used to approximate a target state or operation. In variational algorithms, its design and initialization crucially affect the accuracy and efficiency of the solution. Continuous-time quantum walk The quantum analogue of random walk, in which the evolution of a quantum system over a graph is continuous in time offering advantages in exploring complex networks and solving combinatorial problems. Cumulants Statistical measures that generalize moments to quantify higher-order correlations and dependencies among random variables. They isolate intrinsic multiway interactions, capturing nonlinear and non-Gaussian structure that ordinary moments or pairwise correlations cannot represent. Curse of dimensionality The exponential increase in data sparsity and computational complexity as the number of features grow. This phenomenon makes it harder for algorithms to detect meaningful patterns and they often require vastly more data for reliable analysis. Dynamic circuits A quantum circuit that incorporates classical processing within the coherence time of the qubits permitting mid-circuit measurements to enable feedforward operations such as using measured values to determine the next gates to be applied. Ensemble learning A machine learning technique that aggregates multiple individual models (known as ‘weak learners’) to produce a strong overall model with high prediction accuracy and robustness. Entanglement A uniquely quantum phenomenon, in which the states of two or more particles become interdependent; consequently measurement of one entangled particle instantaneously affects the state of the other particle. Exponential speedup A performance improvement of an algorithm whereby running time decreases exponentially compared to the state-of-the-art algorithmic benchmark. Fidelities of 1-qubit and 2-qubit gates Accuracies in quantum operations that create entanglement between qubits in a 2-qubit gate. If reduced, the effectiveness of quantum computations is decreased, and the complexity of solved problems is more limited. Graph neural networks (GNNs). A class of neural networks designed for graph-structured data, which aggregate and update node features based on neighbouring nodes while learning both local and global graph properties. Kernel methods A class of algorithms that use kernel functions, enabling them to transform data features to a high-dimensional, implicit feature space without exactly computing coordinates in the transformed feature space. k-means An unsupervised algorithm that partitions data into k clusters by assigning each point to the nearest centroid. k-means iteratively minimizes within-cluster variance and is widely used in unsupervised learning. Laplacian A matrix representation of graph connectivity that combines degree and adjacency matrices of the graph, enabling spectral clustering and dimensionality reduction of graphs. Logical qubits A higher-level abstraction of qubits used in a fault-tolerant quantum device where a single qubit is encoded using a collection of physical qubits while protecting quantum information from errors. Neutral atom qubits Qubits encoding quantum information in the internal states of individual, electrically neutral atoms held in optical or magnetic traps, offering long coherence times and scalability for quantum computing applications. Nondeterministic polynomial-hard A class of computational problems that are at least as hard as problems in the nondeterministic polynomial complexity class, meaning that finding an efficient solution for them would imply efficient solutions for all problem in nondeterministic polynomial. Non-negative matrix factorization A linear algebraic method to factor a data matrix V to two lower-rank non-negative matrices W and H representing parts of the original data matrix. Optimal transport A mathematical framework for finding the most cost-effective map from one probability distribution to another. Quantum approximate optimization algorithm A quantum algorithm that produces approximate solutions for combinatorial optimization problems. Quantum circuits Structured sequences of quantum gates operating on qubits to perform quantum computation. Quantum convolutional neural networks (QCNN). The quantum analogue of convolutional neural networks, in which quantum circuits perform convolution and pooling operations; it efficiently extracts hierarchical features from quantum data and has applications in quantum state classification and error correction. Quantum network medicine An emerging interdisciplinary field that leverages quantum computing and network science to analyse complex biological systems by applying quantum-enhanced techniques to biological interactions and reveal insights into disease mechanism and biomarker discovery. Quantum neural network (QNN). A quantum machine learning model that utilizes parameterized quantum circuits inspired by classical neural networks. Qubits The basic units of quantum information, analogous to classical bits. Unlike bits, qubits exist in a combination or superposition of 0 and 1 simultaneously, providing the foundation for quantum algorithms that can process information leveraging fundamentals of quantum mechanics. Random walk A stochastic process in which a walker on a graph starts at a vertex and repeatedly moves to one of its neighbouring vertices, chosen at random, generating a path through the vertices. Sample limited Refers to problems that are highly constrained in the number of samples, particularly with respect to the size of the feature space. Spin qubits Qubits storing quantum information in the intrinsic spin states of particles like electrons or nuclei, often implemented in semiconductor quantum dots and controlled through magnetic or electric fields for quantum operations. Superconducting qubits Qubits that can exist in multiple states simultaneously based on superconducting circuits operating at extremely low temperatures to minimize thermal noise and maintain coherence, enabling fast gate operations for high-speed quantum computations. Superposition A fundamental principle in quantum mechanics stating that a quantum system can exist in multiple states simultaneously. Tensor decomposition A generalization of matrix factorization to multidimensional arrays or tensors, decomposed into latent lower-dimensional factors that capture key patterns in complex data. Topological data analysis (TDA). A mathematical method rooted in algebraic topology that analyses the inherent structure of data to reveal clusters, holes and voids in high-dimensional datasets that conventional data analysis techniques typically overlook. Topological qubits Quantum bits encoded in non-local properties of topological quantum systems, making them inherently resistant to local noise and decoherence. Transformer networks A deep learning architecture that uses self-attention mechanisms to capture long-range dependencies within a sequence and to draw inference. Trapped-ion Qubits Qubits that encode information in the electronic and nuclear spin states of individual ions, controlled using electromagnetic fields enabling long coherence times, making them suitable for high-accuracy quantum operations. Variational quantum classifiers (VQC). A quantum machine learning model that uses parameterized quantum circuits to classify data, with parameters optimized through a classical optimization method to minimize a cost function. 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