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Learn more: PMC Disclaimer | PMC Copyright Notice J Am Heart Assoc . 2026 Feb 24;15(5):e047431. doi: 10.1161/JAHA.125.047431 Search in PMC Search in PubMed View in NLM Catalog Add to search From Artificial Intelligence to Quantum Computing: Promise and Prudence in the Field of Cardiovascular Medicine Mohammed Ayyad Mohammed Ayyad , MD 1 Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA Find articles by Mohammed Ayyad 1, ✉ , Eugene Kim Eugene Kim , MD 1 Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA Find articles by Eugene Kim 1 , Joseph Allencherril Joseph Allencherril , MD 2 Department of Cardiovascular Medicine, University of Texas Medical Branch, Galveston, TX, USA Find articles by Joseph Allencherril 2 Author information Article notes Copyright and License information 1 Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA 2 Department of Cardiovascular Medicine, University of Texas Medical Branch, Galveston, TX, USA * Correspondence to: Mohammed Ayyad, MD, Department of Medicine, Rutgers New Jersey Medical School, 185 S Orange Ave, Newark, NJ 07103. Email: [email protected] , [email protected] ✉ Corresponding author. Received 2025 Oct 22; Accepted 2026 Jan 16; Collection date 2026 Mar 3. Keywords: artificial intelligence, cardiovascular imaging, magnetocardiography, precision medicine, quantum computing Subject Categories: Diagnostic Testing, Electrophysiology, Machine Learning, Cardiovascular Disease, Health Services © 2026 The Author(s). Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13055744 PMID: 41733032 Classical digital systems operate using bits that take values of either 0 or 1. These bits store information in a binary, deterministic framework in which at any moment, each bit is firmly in one state or the other. Classical processors then manipulate these bits using logic gates to perform calculations. 1 This architecture is robust and intuitive, but certain biomedical problems such as molecular simulation, high‐dimensional optimization, and reconstruction of noisy imaging data quickly become computationally intractable because classical systems must evaluate possibilities one at a time. A CLINICIAN’S PRIMER TO QUANTUM SCIENCE Quantum systems differ fundamentally in that they rely on qubits rather than bits. A qubit can exist in a superposition of 0 and 1 simultaneously. The double‐slit experiment provides a useful analogy: particles such as electrons behave like waves, passing through both slits simultaneously and creating interference patterns. In quantum computing (QC), superposition allows a qubit to explore multiple computational paths in parallel rather than sequentially. 2 When many qubits interact, the number of possible quantum states grows exponentially, giving quantum devices access to a computational landscape that classical systems cannot efficiently represent. A second key principle is entanglement, a quantum phenomenon whereby ≥2 qubits are described by a single joint quantum state. As a result, measurement outcomes on each subsystem exhibit strong nonlocal correlations, regardless of spatial separation. These correlations reflect the shared quantum description of the system rather than causal influence. 2 Entanglement enables compact representation and manipulation of many‐body quantum states, making it particularly valuable for problems involving correlated electronic systems and molecular simulations that are inefficient to encode classically. Quantum systems are also governed by the uncertainty principle, whereby certain physical properties (such as position and momentum, or energy and time) cannot be known simultaneously with unlimited precision. 3 Although this imposes constraints, it also makes qubits extremely sensitive to their environment. Quantum sensors exploit quantum coherence and extreme sensitivity of quantum states to detect minuscule magnetic or electromagnetic fields that would be invisible to classical devices, enabling technologies such as quantum magnetocardiography. Because quantum states are fragile, they are susceptible to decoherence, the process by which qubits lose their quantum behavior through interactions with the environment. The Copenhagen interpretation provides the conceptual basis: a quantum state exists as a probability distribution until it is measured, at which point it collapses into a definite outcome. 4 Quantum algorithms and programming paradigms are designed to manage this probabilistic behavior so that the final measurement yields clinically meaningful results. Another useful concept is quantum tunneling, where particles can pass through energy barriers that would be insurmountable in classical physics. In computation, tunneling allows quantum systems to escape local minima—an advantage that parallels its medical uses in molecular imaging, nanoscale diagnostics, and probing electron transfer in living systems. 5 Taken together, these principles allow quantum computers to explore vast solution spaces more efficiently than classical machines, particularly in tasks involving simulation, search, and optimization. Quantum sensors leverage the same principles for measurement rather than computation, enabling diagnostic tools that can detect physiologic signals with unprecedented efficiency and precision. QUANTUM TECHNOLOGIES RELEVANT TO CARDIOVASCULAR MEDICINE Quantum technologies relevant to health care currently fall into 4 categories: QC for computational acceleration, quantum sensing (QS) for physiological measurement, quantum communication and key distribution for secure data transfer, and quantum random number generation for high‐quality cryptography and simulation. Quantum Sensing: The Most Clinically Mature Quantum Technology QS is the most clinically advanced quantum technology in cardiovascular medicine. Quantum sensors leverage the extreme responsiveness of quantum states to small magnetic or electromagnetic disturbances, allowing detection of cardiac electrical activity with a level of precision that classical sensors cannot match. Two primary quantum sensor platforms are relevant for cardiovascular applications. The first is the superconducting quantum interference device, which measures femtotesla‐level magnetic fields by detecting changes in the phase of a superconducting loop. Superconducting quantum interference device magnetometers have been used in physics for decades but require cryogenic cooling, which historically limited their use in medicine. 6 The second platform relies on nitrogen‐vacancy centers in diamonds. These solid‐state quantum defects behave like highly sensitive magnetometers that can operate at room temperature. 7 Nitrogen‐vacancy sensors emerge as a more practical alternative to superconducting quantum interference device technology because of their ability to eliminate the need for large cryogenic systems while offering comparable sensitivity. Electrical activity in the heart generates magnetic fields roughly 1 million times weaker than the ambient magnetic noise in a hospital environment. Standard surface ECG captures voltage changes on the skin but loses fine spatial detail and cannot reconstruct 3‐‐dimensional conduction patterns. Quantum sensors, by contrast, can detect the underlying magnetic fields directly. By taking advantage of cardiac magnetic signals' ability to pass through tissue with minimal distortion, quantum magnetocardiography provides better spatiotemporal representation of cardiac conduction than conventional ECG. This enables high‐resolution electrophysiologic mapping from outside the body, without catheters or radiation. This was demonstrated in the MagMa Study, which evaluated a quantum magnetocardiography system in patients with suspected nonischemic cardiomyopathy. The system achieved a sensitivity of 94.7% and a specificity of 98.5% for detecting cardiomyopathy, significantly outperforming surface ECG and echocardiography in borderline cases. 8 Beyond cardiomyopathy detection, quantum magnetocardiography has multiple electrophysiology applications supported by growing data. Because cardiac magnetic fields are preserved more faithfully than surface voltages, magnetocardiography can reconstruct activation patterns with high spatiotemporal resolution. Multichannel magnetocardiography systems have successfully generated noninvasive 3‐dimensional maps of ventricular excitation, with inverse‐solution methods achieving root‐mean‐square errors <10 mV for action‐potential amplitude estimation at signal‐to‐noise ratios of at least 20 dB and accurately identifying small low‐amplitude regions corresponding to scar or ischemia, demonstrating the potential for magnetocardiography to detect diseased myocardium without the need for invasive catheter‐based mapping. 9 Furthermore, magnetocardiography mapping in atrial fibrillation has demonstrated organized rotational activation patterns that emerge and shift abruptly during the arrhythmia. These rotational segments can persist for only a few cycles or extend for 10s of seconds, reflecting dynamic changes in atrial activation that are largely undetectable on surface ECG. The ability of magnetocardiography to capture and visualize these transient, spatially complex conduction patterns in real time underscores its superior temporal resolution and enhanced sensitivity to atrial electrophysiologic behavior. 10 Magnetocardiography parameters also correlate strongly with invasive markers of scar‐related slow conduction. For instance, fragmentation metrics correlate well in patients with anterior infarction, and these abnormalities normalized after surgical ablation, coinciding with noninducibility of ventricular tachycardia in 20 of 21 patients. 11 Furthermore, magnetocardiography has localized the origin of ventricular arrhythmias including premature ventricular contractions and ventricular tachycardia within a few millimeters compared with catheter‐based mapping, validated in patients with both spontaneous and paced beats. 12 Together, these findings suggest that quantum magnetocardiography could support noninvasive localization of arrhythmogenic substrates, shorten mapping time, reduce radiation exposure, and decrease the need for extensive intracardiac manipulation during ablation procedures. QS also holds potential for ischemic disease. Quantum magnetometers detect minute changes in depolarization and repolarization kinetics. Early pilot work, although preliminary, suggests that magnetic signatures of ischemia appear earlier than voltage changes on ECG. 13 , 14 In emergency medicine, this could compress diagnostic timelines for patients with chest pain or ambiguous ECG findings, potentially aiding triage and reducing unnecessary admissions. Drug Discovery and Molecular Cardiology Drug discovery is a natural target for quantum computation as many of the most important cardiovascular drug targets involve complex electronic structures and many‐body interactions that classical computers struggle to simulate with accuracy and in a timely manner. Quantum systems can, in principle, model these interactions directly rather than relying on approximations that often fail for large or highly correlated molecules. This capability is especially relevant in cardiovascular medicine, where sarcomeric proteins, ion channels, lipid‐metabolizing enzymes, and redox‐sensitive signaling pathways are tightly interdependent with a multitude of steps governed by quantum‐scale electronic behavior. Recent demonstrations highlight the magnitude of these potential gains. Quantum phase estimation algorithms have reduced the computational burden of simulating pharmaceutically relevant protein‐drug complexes by orders of magnitude. As one example, simulation of the Bruton’s tyrosine kinase inhibitor ibrutinib, which would require >1000 years using classical Trotterization methods, can be performed in a matter of days using sparse qubitization on error‐corrected quantum architectures. 15 Although not yet clinically deployed, these platforms illustrate the potential of simulating molecular interactions before laboratory testing and modeling cardiovascular drug interactions at atomic precision. In addition, quantum machine learning and quantum chemistry frameworks have demonstrated advantages in molecular property prediction and docking simulations, with models outperforming classical baselines in predictive accuracy and throughput in certain settings. 16 , 17 , 18 Hybrid workflows such as HypaCADD have shown that quantum models can match or exceed classical algorithms in mutation‐impact prediction, a critical step in assessing how genetic variants alter drug binding. 18 In tests on SARS‐CoV‐2 protease mutants, the quantum neural network captured mutational effects as well as or better than classical models, suggesting that quantum feature spaces may detect structural or electronic changes that classical methods overlook. Importantly, these results were achieved on commercial quantum processors demonstrating that such quantum modules can operate reliably on current hardware and signaling early progress toward potential quantum advantage in specific components of drug‐design workflows. 18 Future‐generation quantum hardware and coding approaches should only improve their performance. These methodological advances have direct implications for cardiovascular medicine. Quantum‐supported computational pipelines may help accelerate the theraputics discovery for hypertrophic cardiomyopathy by enabling higher‐fidelity modeling of sarcomeric protein dynamics, refine antiarrhythmic drug design by improving prediction of ion‐channel interactions and behavior, and support the development of next‐generation lipid‐modifying agents by characterizing complex enzymatic pathways involved in cholesterol metabolism. Quantum Computing in Cardiovascular Imaging and Hemodynamic Modeling For cardiac imaging, quantum algorithms such as quantum annealing‐based computed tomography have demonstrated the ability to reconstruct images with higher accuracy and better noise suppression than conventional methods under optimal conditions. In one study, quantum annealing‐based computed tomography required only 2 qubits per pixel and, with abundant projections and low noise, outperformed classical algorithms in image quality. However, with limited projections or higher noise, classical methods still performed better. 19 Quantum imaging techniques also leverage quantum metrology to reduce quantum noise and improve resolution, potentially allowing for lower radiation doses while maintaining diagnostic quality. 19 Evidence from related clinical imaging tasks supports this trajectory. In one study using based computed tomography images from COVID‐19 patients, a quantum neural network constructed with quantum machine‐learning techniques outperformed classical deep‐learning models by >2.92% in accuracy and achieved an average recall of ∼97.7%. The quantum neural network also trained substantially faster, requiring 52 minutes compared with 1 hour and 30 minutes, likely due to the faster convergence properties of quantum optimization when handling large, biased clinical data sets. 20 These results demonstrate that quantum models can offer measurable performance and efficiency gains in medical image classification tasks relevant to real‐world clinical workflows. Looking ahead, quantum‐assisted modeling could pair anatomical imaging with real‐time physiologic simulation. In structural heart disease or transcatheter valve interventions, quantum‐enabled approaches may incorporate hemodynamic modeling directly into imaging pipelines, enabling preprocedural prediction of paravalvular leak or dynamic flow patterns. 20 Such capabilities could guide device selection and deployment strategies in the cardiac catheterization laboratory, reduce complications while improving procedural precision and efficiency. CLARIFYING THE BROADER QUANTUM LANDSCAPE: COMMUNICATION, RANDOMNESS, HARDWARE FEASIBILITY, ALGORITHMIC PATHWAYS, AND QUANTUM ADVANTAGE Quantum key distribution, quantum random number generation, and quantum analog simulators represent additional branches of quantum science with distinct capabilities and future relevance for health care, even though they currently lack direct cardiovascular applications. Quantum key distribution provides secure communication channels whose confidentiality is ensured by quantum physics rather than computational models. Any attempt to intercept quantum‐encoded information introduces detectable disturbances, making quantum key distribution a potential future tool for protecting the transfer of multimodal clinical data sets, genomic information, or remote sensing data. As federated learning and cloud‐based analytics expand, quantum key distribution may become important infrastructure for preserving patient privacy. Quantum random number generation uses intrinsic quantum randomness to produce high‐fidelity random numbers essential for cryptography, secure simulation, and unbiased model initialization. Although not directly diagnostic, quantum random number generation could ultimately support health care cybersecurity, secure electronic health records, or secure simulation‐heavy drug discovery workflows. Quantum analog simulators emulate specific physical systems using engineered quantum interactions. They may eventually enable detailed modeling of protein folding, ion‐channel behavior, or drug–receptor energetics. Although not yet applied to cardiology, analog simulation represents a potential bridge between current QS applications and future QC–driven molecular discovery. Although no QC study in cardiovascular medicine has yet demonstrated such a “quantum advantage,” QS has shown diagnostic performance that approaches or surpasses current clinical standards. A practical distinction must be made between quantum applications with credible near‐term pathways and those that remain distant. The most promising opportunities over the next several years arise from methods that pair quantum components with classical workflows, including quantum‐enhanced pattern recognition, hybrid quantum neural networks, and small‐molecule simulation techniques used in early‐stage drug design. These approaches align with realistic cardiovascular use cases such as multiomics risk prediction, image reconstruction, and modeling protein–drug interactions. In contrast, several frequently discussed ideas such as fully quantum electrophysiologic simulation, whole‐heart biophysical modeling, and real‐time interventional guidance remain aspirational. These would require levels of hardware stability, scale, and error correction far beyond current capabilities. Distinguishing between problems that quantum systems can plausibly address in the near term and those that require major future advances helps ensure that clinical enthusiasm remains grounded in achievable benefit rather than speculative promise. To contextualize feasibility and manage expectations, the article includes Table 1 outlining potential near‐term (1–5 years), midterm (5–10 years), and long‐term (>10 years) cardiovascular applications with corresponding readiness levels. This framework enables clinicians to distinguish areas where quantum technologies may have immediate impact from those requiring substantial advances in hardware, algorithms, and validation. Table 1. Framework Outlining the Anticipated Clinical Timelines for Quantum Technologies in Cardiovascular Medicine, Detailing Representative Applications, Supporting Evidence, and Translational Readiness Time horizon Application area Representative use cases Quantum modality Evidence level Near term (1–5 y) Electrophysiology diagnostics Noninvasive detection of nonischemic cardiomyopathy; high‐resolution magnetocardiography; atrial fibrillation activation mapping; localization of ventricular arrhythmias; identification of scar‐related slow conduction Quantum sensing (superconducting quantum interference device, nitrogen‐vacancy‐diamond) Pilot and early clinical studies with high sensitivity and specificity; validated inverse‐solution mapping approaches Early ischemia detection Magnetic signatures preceding ECG changes; triage of chest pain with nondiagnostic ECG Quantum sensing Preliminary human studies describing early ischemic features Cardiac safety screening Detection of drug‐induced conduction abnormalities during early drug development Quantum sensing Conceptual feasibility demonstrated Midterm (5–10 y) Molecular cardiology and drug discovery Quantum‐enabled modeling of sarcomeric proteins; ion‐channel binding prediction and affinity estimation; docking for lipid‐modifying therapies; mutation‐impact prediction Quantum computing (hybrid QC/quantum machine learning) Quantum chemistry acceleration demonstrated; QNNs outperforming classical models in selected tasks Cardiovascular imaging Quantum‐assisted CT/magnetic resonance imaging reconstruction; noise‐suppressed image reconstruction; accelerated classification Quantum computing + quantum metrology Quantum annealing–based CT demonstrates superior performance under optimal conditions; QNN imaging models with improved accuracy Hemodynamic modeling Quantum‐accelerated blood‐flow modeling; prediction of paravalvular leak; preprocedural simulation Quantum computing Algorithmic feasibility demonstrated in proof‐of‐concept studies Long term (>10 y) Whole‐heart electrophysiologic simulation Full quantum modeling of 3‐dimensional cardiac activation and propagation; real‐time ablation guidance Fault‐tolerant large‐scale quantum computing No current hardware capable of implementation; conceptual only Personalized quantum digital twins Integration of multiomics and imaging into quantum‐enhanced patient‐specific models Hybrid QC + QS ecosystem No functional prototypes; major engineering barriers remain Fully quantum diagnostic pipelines End‐to‐end quantum‐enabled diagnostic systems QC + QS + quantum communication Entirely conceptual at present Open in a new tab CT indicates computed tomography; QC, quantum computing; QNN, quantum neural network; and QS, quantum sensing. A Call to Engagement Quantum technologies are advancing along divergent trajectories, with QS already demonstrating clinically relevant performance and QC progressing more slowly due to hardware noise, limited qubit counts, and the need for scalable error correction ( Figure ). As occurred with earlier computational transitions in cardiology from digital ECG systems to deep‐learning–enabled imaging, meaningful progress requires early clinical engagement to ensure that technological development aligns with patient‐centered outcomes rather than hardware benchmarks. Figure 1. Overview of emerging quantum applications in cardiovascular medicine. Open in a new tab The diagram illustrates 4 domains: quantum sensing for noninvasive electrophysiologic and ischemic assessment; quantum drug discovery and design for molecular simulation and target prediction; quantum computing for enhanced imaging, machine learning, and hemodynamic modeling; and quantum communication and security for protecting genomic data and digital health infrastructure. 3D indicates 3‐dimensional; and AI, artificial intelligence. Interdisciplinary collaboration among clinicians, computational scientists, quantum engineers, and professional societies will be essential to codevelop protocols that assess clinical end points, diagnostic accuracy, and cost efficiency. Developing literacy in quantum science may help to identify cardiovascular problems where quantum methods offer plausible benefit, to distinguish mature modalities such as magnetocardiography from speculative applications that depend on future architectures, and to participate in setting realistic evaluation standards. Ultimately, the trajectory of quantum medicine will be determined not only by innovation but also by the capacity to guide its implementation. Methodologically rigorous and clinically grounded evaluation will be essential to ensure that quantum technologies—whether sensing or computational—evolve in alignment with clinical priorities and meaningfully advance cardiovascular care. Sources of Funding The authors have no funding source to declare. Disclosures All authors have no conflicts of interest or financial ties to disclose. Acknowledgments The Figure was created with BioRender.com (BioRender attribution key: Kim, E. 2026 | https://BioRender.com/bu3m3u1 ). 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BMC Med Inform Decis Mak. 2021;21:227. doi: 10.1186/s12911-021-01588-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Articles from Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease are provided here courtesy of Wiley ACTIONS View on publisher site PDF (546.7 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top