Quantum Machine Learning and Applications in Health Care


Quantum computing has recently become accessible through a combination of commercially accessible hardware, high-level language support, and CPU-based simulators. Quantum computing is a largely foreign concept to most that boasts dramatic improvements in compute efficiency. But the technology itself and how it can be used to advance computationally intensive applications are often misunderstood, with limited examples for applied practitioners less familiar with low-level programming and quantum concepts. In this session, we will provide examples of when quantum computing is best applied to accelerate health care-specific applications and biomedical research. We will also provide an overview of how quantum computing works and a short overview of how to leverage open-source libraries, specifically Qiskit and Q#, to build, train, and evaluate a machine learning model for breast cancer prediction using an open dataset. We will also review how to build and run these models on local simulators and how these algorithms can be deployed on quantum hardware through cloud providers such as Azure.

Background Knowledge:

Should be familiar with Python and/or .NET


Dr. Schulz is a physician scientist with a background in computational healthcare, molecular biology, and virology. Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has a research interests in the management of large, biomedical data sets and the use of real-world data for predictive modeling. At Yale School of Medicine, he has led the deployment of the organization’s data science infrastructure which consists of a composable computing infrastructure to support the development of biomedical AI applications. Dr. Schulz is also a co-founder of Refactor Health, a digital health startup focused on the development of AI-driven digital signatures and automated healthcare DataOps.

Open Data Science




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