How Cleveland Clinic is making quantum computing a reality

Cleveland Clinic and its IBM-managed quantum computer are taking on the task of exploring how quantum computing can optimize healthcare's complex systems and drug discovery and seeing if it can provide more accuracy than AI models. 

In March, Cleveland Clinic teamed up with IBM to become one of the first health systems in the world to install a quantum computer that would be strictly focused on healthcare research. 

Since the installation, Cleveland Clinic has launched nine initiatives to explore how quantum computing can optimize healthcare. 

Lara Jehi, MD, chief research information officer of the health system, spoke to Becker's about the quantum computing research Cleveland Clinic is doing, how this technology puts the health system in a unique place and what is needed for quantum computing to make an impact in healthcare.

Question: What advances have been made since the quantum computer was installed at the health system?

Dr. Lara Jehi: We launched nine projects using quantum and we're in the process of developing eight more. These projects are being run by Cleveland Clinic in partnership with IBM research. 

We are also building an ecosystem of research around quantum, and we are also launching partnerships with local universities so we can build a pipeline for future engineers, computer scientists and scientists who have quantum expertise.  

We've also secured two grant awards for quantum research and medicine.

Q: What ways can healthcare benefit from the power of quantum computing?

LJ: When we think about quantum computing applications, we think about them in three categories: quantum simulations, quantum machine learning and quantum optimization. 

Quantum simulations are very essential in developing drugs and for drug discovery. This concept can accelerate how we can develop therapies, therefore enhancing how we can take better care of people. 

Quantum machine learning is a more general concept. Most advanced AI models that exist now struggle with accuracy and reproducibility. We're exploring if quantum can do a better job. 

And third is quantum optimization. Quantum computing is pretty good at going through data quickly and making links that are otherwise not visible with regular computers. 

In healthcare, we have many complex systems, such as supply chains, that we need to optimize. Right now, a lot of optimization in healthcare is relying heavily on AI, but quantum could be the next frontier that could accomplish this better than AI.

Q: What are some of the benefits and challenges associated with being one of the first health systems to explore quantum computing?

LJ: One of the biggest challenges that we had was building a team around this. This is new technology, and we are biomedical researchers. We think in biology. And then you bring in cutting edge technology like quantum and the teams that work within that realm are all engineers, but not everyone is a quantum scientist.

So we have to get these people together to come up with projects and work together. That was difficult. It takes time. It takes expertise. 

Q: How do you decide which projects you're going to jump on and initiate with the team?

LJ: While the quantum was being built physically in Cleveland Clinic, we already had started getting the teams together and organizing the research, and we have a competitive review process. 

We put out a con quarterly, and we guide the agenda for what strategic priorities we want the teams to focus on. They then submit scientific research proposals, and we put those through a federal scientific review committee. 

Q: How can quantum computing reach its full potential in healthcare?

JL: We still have a lot to learn from it. We need to figure out what type of scientific questions are appropriate to be asked in a quantum computing approach. We don't want to be wasting a lot of time asking the wrong questions, and investing in the wrong types of projects.

We also need quantum computers to be able to handle more data and deeper levels of computation so that they can be more stable in the results that they provide.


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