How can quantum computers be used in healthcare?

Comparing quantum computers and traditional computers

Quantum computing is an entirely new method of computing compared to the classical computing of today's supercomputers, desktops, laptops and mobile devices.

Classical computing uses binary bits -- the familiar 1s and 0s that encode logic and data. Each transistor in a computer chip can be either on or off; in electrical terms, voltage can be either high or low.

In contrast, a quantum bit -- also known as a qubit, the basic unit of quantum computing -- can exist in different states simultaneously, meaning both 1 and 0 and any combination in between. This feature enables quantum computers to test many more computer simulations and process certain types of calculations faster than classical computers.

Indeed, simulations are an important tool for solving complex problems in healthcare and life science, according to Dr. Lara Jehi, chief research information officer at Cleveland Clinic. For example, drug development starts with simulating chemical compounds to find out if they work.

"There are thousands of compounds that a company decides they want to test and then try them in experiments in the lab on human cells," Jehi said. "They have models, and then they do the clinical trials on people before we eventually find one drug that actually works if we are lucky."

The length and complexity of the process is why developing a drug can cost at least $10 million and take 10 years or more, she said.

"With quantum computing, because it is better at simulation, the hope is that it can narrow down the list of chemical compounds that need to be developed and tried significantly enough that it will become much faster and cheaper to develop new drugs," Jehi said.

Key use cases for quantum computers in healthcare

With health and life science data continuing to grow exponentially, hospitals, pharmaceutical companies and universities are looking to exploit the quantum mechanics properties of quantum computers to use qubits to store much larger volumes of data than classical computers.

The possibilities have led to partnerships among quantum computing vendors, hospitals, pharmaceutical companies and research organizations. These, in turn, have resulted in pilot projects for using quantum computers to accelerate innovation in healthcare and life science.

Here are a few case study examples.

Gene interaction simulation

At Texas A&M University, researchers are using quantum computing to predict gene expression, the process by which the information encoded in genes produces proteins and certain molecules. It could be the key to individually targeted cures for genetic diseases and cancer. According to research findings published in Npj Quantum Information, applying quantum computing to biology could improve understanding of single-cell gene regulatory networks by more effectively showing the relationship between genes than is possible with conventional statistical methods.

Solving protein design challenges

Menten AI, a biotechnology company that designs peptide and protein therapeutics, used a hybrid of classical and quantum computing from D-Wave to solve protein design problems. The company claims the approach can create better proteins and, ultimately, enable new drug discoveries.

The hope is that it can narrow down the list of chemical compounds … significantly enough that it will become much faster and cheaper to develop new drugs.

Enhanced medical imaging

Quantum computer manufacturer Rigetti Computing publicized a proof of concept for quantum machine learning to improve the performance of a standard AI model for identifying breast cancer and pneumonia using MedMNIST, a large-scale collection of biomedical images.

Drug discovery

Pharmaceutical and life science company Merck has used quantum computing to develop materials and treatments for diseases such as cancer. Merck executives said quantum mechanics enables new perspectives.

Protein structure prediction

Cleveland Clinic and IBM recently published research that outlines a framework for applying quantum computing methods to predict protein structures. Accurately predicting protein structures could improve researchers' understanding of how diseases spread and how to develop effective therapies.

Predictions about commercial availability vary

Healthcare and life science companies are pushing to move beyond pilot projects to commercially viable products, but the prognosis remains mixed.

Murray Thom, vice president of quantum technology evangelism at D-Wave, said commercial customers in the healthcare industry are already benefiting from D-Wave's quantum offerings.

For example, Japan Tobacco, a tobacco company that also makes pharmaceuticals, uses D-Wave's quantum computing technology to optimize the AI training process for drug design and improve the speed and quality of drug discovery. Biotechnology company PolarisQB uses hybrid quantum technology to expedite drug discovery by identifying thousands of lead molecules that can fulfill a set of stringent criteria, all in a fraction of a second.

Still, while much of the research points to progress in labs and in early prototypes, Gartner analyst Michael Shanler said commercial success is much further down the road.

"I think we are looking at the mid-2030s before we start to see quantum computing as a regularly used technique in drug discovery," Shanler said, adding it will likely follow the same timeline as regular use of quantum computing in other applications, such as advanced imaging for tumor diagnosis and care plans.

Potential drawbacks of quantum computers in healthcare and life science

Despite all the pilot projects and investments, analysts say there are significant challenges to advancing use of quantum computing in healthcare and life science environments.

Prominent barriers include the following:

• The potential for quantum computers to break encryption algorithms, which could result in breaches in legally protected health data.
• The loss of quantum properties, known as decoherence, and external noise that make quantum computers prone to errors.
• The difficulty, not to mention the computational and energy demands, of correcting errors.

Shanler elaborated on other barriers to consider.

For one thing, pharma companies aren't good at quantum chip design, which means they'll have to partner with quantum "powerhouses," apply the right skill sets and work with both quantum and AI teams to learn how to program and translate health informatics scripts for quantum environments.

He added that the learning curve has been more difficult than most people originally realized. "A lot of the earlier proofs of concept that have been run in the quantum space have been really more learning activities to teach pharma companies how to design for quantum," he said.

Best practices for adopting quantum computers in healthcare

Healthcare stakeholders who are starting on their quantum journey should consider key questions and best practices to improve efficiency, save money and drive overall performance in their quantum R&D efforts.

D-Wave's Thom suggested answering the following questions, which can point to best practices:

• Are real-world customers using the product being considered?
• What are the hardware's limitations?
• How reliable is cloud access to quantum systems?
• How long does it take to get results after submitting a problem?
• Does the provider offer training to upskill your workforce or provide professional services to guide you on the quantum journey?

Cleveland Clinic's Jehi said healthcare and life science companies must bring a keen sense of urgency to educating themselves about the technology.

"Health systems should start learning about quantum and not wait for the technology to be ready for commercial use," she said. "There's so much to learn. It's a complex technology, and we have to invest in learning it, trying to understand it and not wait for all of that to happen."

Nicole Lewis is an independent business and technology journalist who covers public policy, technology and business issues.