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May 21, 2025

Study finds quantum computing in health care faces significant challenges, but there is promise

Common models in quantum machine learning. Quantum circuit depictions of linear vs. non-linear embedding in quantum models. Credit: npj Digital Medicine (2025). DOI: 10.1038/s41746-025-01597-z
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Common models in quantum machine learning. Quantum circuit depictions of linear vs. non-linear embedding in quantum models. Credit: npj Digital Medicine (2025). DOI: 10.1038/s41746-025-01597-z

A broad systematic review has revealed that quantum computing applications in health care remain more theoretical than practical, despite growing excitement in the field.

The comprehensive study in npj Digital Medicine, which analyzed 4,915 research papers published between 2015 and 2024, found little evidence that quantum machine learning (QML) algorithms currently offer any meaningful advantage over classical computing methods for health care applications.

"Despite in research claiming quantum benefits for health care, our analysis shows no consistent evidence that quantum algorithms outperform classical methods for clinical decision-making or health service delivery," said Dr. Riddhi Gupta from the School of Mathematics and Âé¶¹ÒùÔºics and the Queensland Digital Health Center (QDHeC) at the University of Queensland.

The review identified several critical gaps in current research approaches:

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Dr. Gupta said that while holds tremendous theoretical promise for health care, this review provides an important reality check on the current state of the technology.

"The field needs to address these methodological challenges before quantum methods can deliver meaningful advantages in health data processing," she said.

The researchers propose new standards for evaluating quantum computing applications in health care, including minimum requirements for demonstrating scalability and performance under realistic conditions.

QDHeC's Deputy Director, Professor Jason Pole, said that this study confirms that while quantum technology is promising, it is not going to change health care next week.

"Decision makers get understandably excited when we talk about the possibilities of quantum computing in health care, but Dr. Gupta's study affirms that we still have a lot of work to do before we can apply this technology in a useful and strategic way."

Senior author of the study, Associate Professor Sally Shrapnel who leads the QDHeC Quantum Program and is Deputy Director of the Australian Research Council's Center of Excellence for Engineered Quantum Systems (EQUS) says that despite these challenges, researchers are optimistic about the future of quantum computing in health care.

"This review captures the current state of play, but the field is advancing rapidly with impressive progress from both universities and companies," she said.

"I have no doubt we will see exciting quantum applications in digital health care in the future."

More information: Riddhi S. Gupta et al, A systematic review of quantum machine learning for digital health, npj Digital Medicine (2025).

Journal information: npj Digital Medicine

Provided by Australian Research Council Centre of Excellence for Engineered Quantum Systems

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Get Instant Summarized Text (GIST)

Quantum computing in health care remains largely theoretical, with current quantum machine learning algorithms showing no consistent advantage over classical methods. Most studies rely on simulations rather than real hardware and overlook key issues such as noise, error mitigation, and scalability. Significant methodological challenges must be addressed before practical benefits can be realized.

This summary was automatically generated using LLM.