There are even more inroads to the first advantage – quantum would enable even stronger disease response.

Enabling Medical Research and Precision Medicine with Quantum Computing

Cleveland Clinic chose to work with IBM on its quantum computing initiative due to its long-term perspective on disruptive and transformative technology. The partnership gives Cleveland Clinic access to IBM’s data sciences, engineers and researchers, allowing their teams to work with IBM’s full spectrum of computation, including high-performance computing, artificial intelligence, machine learning and quantum computing.

The partnership also involved the installation of a quantum computer on the Cleveland Clinic campus. According to Jehi, it’s the only place in the world that has a quantum computer fully dedicated to healthcare and life sciences.

“That gives us the flexibility to ask questions, take risks and be more aggressive in figuring out this technology,” she says, adding that quantum computing is pushing Cleveland Clinic to think about research questions differently.

The organization made a huge investment to obtain the quantum computer, but Jehi emphasizes that Cleveland Clinic is not adopting the technology for its own benefit, but because it sees it as the technology of the future.

“We are very interested in building teams across the spectrum of healthcare, academia and biomedical research that would be interested in partnering with us and collaborating with the focus of figuring out what quantum means for healthcare,” she says.

The health system’s main goal for the technology is to accelerate how it approaches biomedical discovery. Since announcing the Discovery Accelerator in 2021 and embarking on the 10-year partnership with IBM, Jehi says the organization has identified three pillars of quantum computing.

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The first is quantum simulations, which is quantum research that transforms a chemical formula into a 3D structure. Quantum simulations are important in fields such as drug discovery, therapeutics and immunotherapy, Jehi explains. They enable the simulation of structures that have been impossible to simulate with today’s tools.

The second pillar is quantum machine learning, which encompasses the computing that AI is not yet able to handle, whether due to models not getting past a certain threshold of accuracy or because it’s too expensive.

“The models require too much data for their input,” says Jehi. “Could quantum machine learning simplify the extent of inputs that need to go into a model to get better predictions?”

The third pillar is quantum optimization, in which quantum computing can optimize processes such as supply chains and the design of clinical trials.

“What they have in common is the ability to use quantum computing for problems that are impossible to solve with classical computing,” says Jehi.