Joint research team selected for quantum computing project to advance cardiovascular disease analysis

A collaborative research team comprising the University of Seoul, Seoul St. Mary's Hospital, and Flowix Inc. has secured a new grant for the "Quantum Computing-Based Quantum Advantage Challenge Research Project" for 2026, organized by the Ministry of Science and ICT and the National Research Foundation of Korea.

The project's core objective is to leverage quantum algorithms to accelerate and improve the accuracy of computational fluid dynamics (CFD) analysis for cardiovascular diseases. The research will also pinpoint specific conditions where quantum computing demonstrably outperforms classical computing in clinical applications. The team includes Professor An Do-yeol, an honorary distinguished professor at the University of Seoul and CEO of Singularity Quantum, along with Professors Jeong Jeong-im, Baek Kyung-min, and Jang Su-yeon from Seoul St. Mary's Hospital's Radiology department, and Professors Yoon Jong-chan (project lead), Choi Young, and Seung Jae-ho from the Cardiovascular and Cerebrovascular Hospital's Cardiology department. Flowix Inc. is represented by CEO Ha Ho-jin and research director Lee Gyu-han.

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We will numerically verify not only the theoretical possibilities of quantum algorithms but also how much circuit resources and measurement counts can be reduced while maintaining the target accuracy.

This initiative specifically targets the numerical verification of when and under what conditions quantum computers can achieve superior performance compared to classical computers – the "quantum advantage." Given that current quantum computers are in the noisy intermediate-scale quantum (NISQ) era, minimizing errors while reducing circuit and measurement counts is paramount. The research, set to run for two years and six months from 2026 with 2.5 billion won in government funding, will begin with developing a 3D deep learning segmentation model to automatically differentiate the heart, left atrium, and aorta. It will then proceed to build a quantum-based hemodynamics model and validate results using 4D Flow MRI on actual patient cases, aiming for over 95% precision in quantum-based CFD compared to classical methods.

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Patient-specific diagnosis and treatment based on hemodynamics are key to overcoming cardiovascular diseases, but they are not yet sufficiently implemented in clinical practice. By applying quantum algorithms and error mitigation techniques to the CFD solver stage, which is a major bottleneck and computationally most expensive, we aim to identify the conditions under which quantum advantage appears in actual clinical settings.

Cardiovascular diseases, characterized by diverse causes and symptoms, pose significant risks of mortality and long-term disability. However, patient-specific diagnosis and treatment based on hemodynamics, the core of the pathophysiology, remain underdeveloped. While anatomical imaging like CT and MRI can reveal vessel narrowing, they struggle to quantify blood flow velocity and pressure, or the wall shear stress impacting clot formation and myocardial damage. CFD analysis can estimate these parameters from anatomical images, but its clinical application has been limited by computational speed delays when increasing grid element counts or handling complex nonlinear problems and boundary conditions.

The research team believes quantum computers can overcome these limitations by processing high-density calculations rapidly. Unlike classical computers that process sequentially using 0s and 1s, quantum computers utilize superposition to explore numerous possibilities simultaneously. To achieve this efficiently and reduce errors, the team will combine four techniques: Variational Quantum Algorithm (VQA) for iterative refinement between quantum and classical computers, Krylov Subspace methods for focused exploration in high-probability solution areas, Classical Shadow measurement techniques for simultaneous estimation of multiple physical quantities, and Non-Markovian Quantum Error Mitigation to mathematically model and remove noise patterns. Seoul St. Mary's Hospital will lead clinical data acquisition and validation, the University of Seoul will develop the quantum algorithms, and Flowix Inc. will verify the accuracy using its automated 4D Flow MRI platform, "Flonics-Streamliner."

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We plan to expand the verification system established in this research to other cardiovascular diseases and various medical imaging-based diagnostic software in the future.