The application of quantum computing in healthcare and life sciences is expected to transform computational medical science. The U.S. federal government has signaled its commitment to a quantum future and its strong support for this emerging field. Exciting applications of quantum technologies include disease diagnosis, drug design, personalized medical intervention strategies, and medical image analysis. Below, we explore some of these promising applications, identifying how today’s research into quantum algorithms translates into more treatment options for doctors tomorrow and better outcomes for patients.
Quantum-assisted machine learning for health diagnostics and data
Medical diagnostics benefit greatly from data-driven insights powered by artificial intelligence (AI) and machine learning (ML). Quantum computing promises to advance AI in medical science by improving the efficiency, accuracy, or speed of certain ML methods. Used to analyze medical data holistically, this could allow doctors to see new patterns in patients’ medical histories and suggest different (and perhaps better) treatments. For example, medical imaging techniques – including CT, MRI and X-ray scans – are vital diagnostic tools. But the resolution of these images is often limited, and tumors or other abnormalities may be missed. Quantum computing has the potential to improve medical image analysis, enhance image-assisted diagnostic methods, and speed up patient treatment time. Improvements such as these will lead to more efficient outcomes and lower costs, as well as giving doctors more time to do what matters most: caring for patients.
Quantum simulation of protein folding
Quantum technology could also greatly improve our understanding of protein folding. In recent years, computational biologists have written impressive algorithms to simulate the shape of proteins. These models allow scientists to better understand the body’s natural processes by illustrating how protein folding determines biological interactions. However, these algorithms still lack the precision needed to achieve the breakthroughs needed to personalize medicine. Future quantum computers may change that.
Scientists believe that one day we will be able to capture the dynamics of larger proteins computationally rather than experimentally, reducing the time and cost of delivering life-saving treatments to patients. In addition, researchers hope to one day make more extensive use of the simulation capabilities of quantum computers, not only to simulate protein structures, but also to simulate key metabolic processes and ensure that treatments have the desired effect.
Small Molecule Drug Design with Hybrid Quantum Algorithms
Another exciting use case for quantum computing is small molecule simulation. To develop new materials and chemicals, researchers typically evaluate thousands of chemical reactions and molecular interactions. Today, quantum computers are already being used in conjunction with state-of-the-art neural networks to help produce small-molecule candidates—a biological compound that, due to its small size, can be rapidly absorbed by the body, making it a valuable type of drug. Scientists believe that more sophisticated quantum computers in the future will be able to generate viable small molecule candidates for experimenters to synthesize and further explore. These simulations will allow biologists to focus their efforts on the most promising candidates, helping them conduct more efficient and accurate experiments. The ultimate downstream effect is a breakthrough treatment for both providers and patients.
The future of quantum in healthcare
While quantum computing may be years away from realizing its full potential, it is gaining momentum in the health and life sciences industries. With the promise of more efficient and reliable diagnostics, future breakthroughs in personalized medicine and targeted therapy, and the promise of an accelerated R&D lifecycle, industry leaders must be prepared to invest in quantum technologies to deliver better outcomes for their organizations and patients.
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