Quantum Kernels for Real-World Predictions Based on Electronic Health Records

• Published in: IEEE transactions on quantum engineering Vol. 3; pp. 1 - 11
• Main Authors: Krunic, Zoran, Flother, Frederik, Seegan, George, Earnest-Noble, Nate, Omar, Shehab
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Configuration management; Datasets; digital health; Electronic health records; electronic health records (EHR); empirical quantum advantage (EQA); Kernel; Kernel functions; Licenses; Machine learning; Measurement; Quantum computers; Quantum computing; quantum kernels; Radial basis function; real-world data; Ruggedness; small data sets; Support vector machines; support vector machines (SVM); Training
• ISSN: 2689-1808; 2689-1808
• DOI: 10.1109/TQE.2022.3176806

Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets, no work done to date has studied empirically whether the quantum advantage is attainable and with what data. In this article, we report the first systematic investigation of empirical quantum advantage (EQA) in healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic health records data subsets and created a configuration space of 5-20 features and 200-300 training samples. For each configuration coordinate, we trained classical support vector machine models based on radial basis function kernels and quantum models with custom kernels using an IBM quantum computer, making this one of the largest quantum machine learning experiments to date. We empirically identified regimes where quantum kernels could provide an advantage and introduced a terrain ruggedness index, a metric to help quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step toward a priori identification of data sets where quantum advantage could exist.