FAU Researchers Develop Quantum Framework to Predict Heart Disease
FAU engineering researchers have developed a novel quantum machine learning framework that significantly improves heart disease prediction
By gisele galoustian | 8/27/2026
Study Snapshot: A research group from Florida Atlantic University’s College of Engineering and Computer Science, led by Arslan Munir, Ph.D., has developed a quantum machine learning framework for heart disease prediction that achieved more than 90% accuracy. Using clinical data from 918 patients, the research group systematically evaluated five quantum feature-mapping techniques and four quantum machine learning classifiers. The best-performing model, a Quantum Support Vector Machine using Angle Encoding, achieved 90.26% accuracy, 92.16% sensitivity, 83.42% specificity and an AUC of 0.93.
The findings suggest the potential of quantum machine learning to capture complex patterns in clinical data and support more accurate disease prediction. The researchers used shallow quantum circuits designed for near-term quantum computing architectures, providing a foundation for future quantum-enabled healthcare applications
Cardiovascular disease remains a major global health challenge, causing millions of deaths each year and imposing substantial healthcare costs. Early, accurate diagnosis is critical for improving outcomes and enabling timely intervention
While conventional machine learning has shown promise in disease prediction, it often struggles with highly complex and nonlinear clinical data
A research group from the College of Engineering and Computer Science at Florida Atlantic University, led by Arslan Munir, Ph.D., professor in FAU’s Department of Electrical Engineering and Computer Science and director of the Intelligent Systems, Computer Architecture, Analytics, and Security (ISCAAS) Laboratory, have developed a novel quantum machine learning framework that significantly improves heart disease prediction, achieving more than 90% accuracy
The study, published in the MDPI AI Journal (impact factor of 6.5), presents a comprehensive evaluation of quantum feature mapping and quantum classification techniques for heart disease prediction, and demonstrates the potential of quantum machine learning to enhance healthcare analytics and clinical decision support systems
Using clinical data from 918 patients, the research group evaluated systematically five quantum feature mapping techniques and four quantum machine learning classifiers to identify the most effective approach for heart disease diagnosis. The best-performing model, a Quantum Support Vector Machine using Angle Encoding, achieved 90.26% accuracy, 92.16% sensitivity, 83.42% specificity, and an AUC of 0.93, highlighting its potential for accurate heart disease prediction
“Our research demonstrates that quantum machine learning can serve as a powerful new paradigm for healthcare analytics,” said Munir. “By leveraging quantum feature representations and quantum-enhanced classifiers, we can model intricate relationships within clinical data more effectively, enabling highly accurate prediction of heart disease. As quantum technologies continue to mature, they have the potential to transform how we diagnose diseases, personalize treatments, and support clinical decision-making.”
The study provides evidence that quantum-enhanced models can deliver highly accurate predictions while maintaining computational efficiency through carefully designed quantum feature maps and shallow quantum circuits
“This work illustrates the growing potential of quantum computing to contribute to advances in healthcare, and medicine,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “Florida Atlantic University is making important investments in quantum computing infrastructure, and the College of Engineering and Computer Science is complementing those investments by building faculty expertise and research capacity in this emerging area. Dr. Munir’s work is an excellent example of how our faculty are exploring the intersection of quantum computing, AI and healthcare. This research also reflects FAU’s broader commitment to building strength in quantum-enabled research and education.”
Conducted through Munir’s ISCAAS Laboratory, the work contributes to ongoing efforts to develop practical applications of quantum technologies in healthcare, cybersecurity, smart infrastructure, autonomous systems, and scientific computing
The publication also complements FAU’s broader investments in expanding our quantum computing ecosystem, industry collaborations, and initiatives focused on preparing students and researchers for the next generation of computing technologies
As governments, industry, and research institutions worldwide invest heavily in quantum technologies, healthcare has emerged as one of the most promising application domains. Quantum-enhanced diagnostic systems could eventually help physicians analyze increasingly complex medical datasets, identify disease risks earlier, and support personalized treatment planning
“Quantum computing is transitioning from theoretical promise to practical innovation, and healthcare represents one of the most impactful application areas where quantum machine learning can make a difference,” said Munir. “Our work provides evidence that quantum approaches can already deliver meaningful results and lays the foundation for future clinical applications powered by next-generation quantum technologies.”
Arslan Munir, Ph.D., professor in FAU’s Department of Electrical Engineering and Computer Science and director of the Intelligent Systems, Computer Architecture, Analytics, and Security Laboratory.
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