Revolutionizing Healthcare with Quantum and High-Performance Computing

Authored By: Bhavya Anoop

Art By: Vanessa Chen Hsieh

As healthcare challenges become increasingly complex, computational tools are emerging as essential methods in transforming disease diagnosis, treatment, and prevention. More specifically, quantum and high-performance computing are two important mechanisms currently driving healthcare innovation. Quantum computing (QC) utilizes the principles of quantum mechanics to perform complex calculations much faster than traditional methods. Due to its speed and accuracy, QC can lead to advances in genomics, treatment planning, and patient data security [1]. Meanwhile, high-performance computing (HPC) breaks down large tasks into smaller, more manageable parts and processes them in a parallel manner. HPC’s efficiency allows for large amounts of data to be processed, and can lead to advancements in fields such as drug discovery, medical imaging, and epidemiology research. Both QC and HPC play key roles in enhancing aspects of healthcare such as enhancing diagnostic techniques to more personalized treatment. By doing so, these technologies can ultimately make healthcare more effective by potentially reducing healthcare costs, improving patient outcomes, and accelerating discovery and development. 

By enabling faster and more accurate computations, QC can overcome limitations in handling vast amounts of healthcare-related information. One of its most significant applications is in diagnostics and genomics. For instance, it can analyze massive datasets to identify genetic markers linked to specific diseases, expediting diagnoses and the creation of personalized treatment plans [2]. Furthermore, quantum encryption methods can advance data security in order to protect against cyber threats [3]. These novel techniques ensure that patient records are safeguarded while allowing them to be analyzed electronically. QC also enhances predictive analytics by processing geographical and genetic data, allowing scientists to react swiftly to unanticipated health hazards or pandemics. This swift response enables early diagnosis of diseases such as COVID-19, thus increasing survival rates, decreasing treatment costs, and lessening its burden on the healthcare system in the long-run [4].

Similarly, HPC plays a crucial role in healthcare by efficiently processing large amounts of data, which has led to advancements in drug discovery efforts and medical image analysis since 2015 [5]. Combining the fields of pharmacology, computational chemistry, and biology, computational drug discovery and design (CDDD) based on HPC uses supercomputers to optimize drug formulations and simulate biological dynamics with greater speed and accuracy [6]. Technologies like grid computing and accelerating hardware such as graphics processing units (GPUs) further improve many CDDD techniques. For instance, grid computing utilizes globally distributed supercomputers that can perform virtual drug screening with increased speed and cost-effectiveness [7]. HPC’s applications also lie in deep learning models, which are a form of machine learning that uses layered artificial neural networks to detect complex patterns in data. This is largely impactful in medical imaging, in which deep learning models can detect diseases and tumors with higher diagnostic accuracy than traditional image analysis methods [5]. This improved accuracy is important for precise and earlier diagnoses but also for reducing misdiagnoses, creating personalized treatment plans, and overall improving patient survival rates. HPC has also been crucial in the field of epidemiology since the 2003 SARS outbreak, when it was used to model an enzyme responsible for the virus’ replication in 3D. Recently, during the COVID-19 pandemic, HPC facilitated large-scale disease modeling and epidemic tracking [5].  In future global health crises, the use of HPC will be essential to rapidly analyze data, detect patterns in the spread of disease, and manage outbreaks.

As these technological advancements reshape the future of healthcare, numerous collaborations and startups are utilizing them to accelerate progress and research. For instance, the COVID-19 HPC Consortium, an joint-initiative led by the White House, the U.S. Department of Energy, and IBM, provided free computing resources for pandemic research [8]. Similarly, the Cleveland Clinic and IBM partnered to establish the Discovery Accelerator, which uses quantum computing for drug discovery and personalized medicine research [3]. Additionally, promising startups have collaborated with pharmaceutical companies to improve clinical trial design by utilizing quantum algorithms. For instance, Zapata Computing, alongside Insilico Medicine, the University of Toronto, and St. Jude Children’s Research Hospital, demonstrated the first model of quantum technology that created more effective cancer drugs than classical models this past year. With quantum-enhanced generative AI technology, researchers were able to focus on KRAS, a protein previously considered impossible to target [9]. 

As QC and HPC become more widely used in this field, numerous other quantum-based healthcare companies and partnerships are emerging across the globe to drive innovation in life sciences. Evidently, these technologies are revolutionizing the healthcare industry by overcoming highly complex obstacles in disease diagnosis, treatment, and prevention. Together, these computing technologies can develop highly effective, targeted therapies that will make disease tracking and treatment more personalized and accurate than ever before. 

References:

1. Chow J. C. L. (2024). Quantum Computing in Medicine. Medical sciences (Basel, Switzerland), 12(4), 67. https://doi.org/10.3390/medsci12040067

2. Tarasov, P. A., Isaev, E. A., Grigoriev, A. A., & Morgunov, A. F. (2020). The utilization of perspective quantum technologies in biomedicine. Journal of Physics: Conference Series, 1439(1), 012040. https://doi.org/10.1088/1742-6596/1439/1/012040

3. Jeyaraman, N., Jeyaraman, M., Yadav, S., Ramasubramanian, S., & Balaji, S. (2024). Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment. Cureus, 16(8), e67486. https://doi.org/10.7759/cureus.67486

4. Gupta, S., Modgil, S., Bhatt, P. C., Jabbour, C. J. C., & Kamble, S. (2023). Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry. Technovation, 120, 102544. https://doi.org/10.1016/j.technovation.2022.102544

5. Li, J., Wang, S., Rudinac, S., et al. (2024). High-performance computing in healthcare: An automatic literature analysis perspective. Journal of Big Data, 11(61). https://doi.org/10.1186/s40537-024-00929-2

6. Liu, T., Lu, D., Zhang, H., Zheng, M., Yang, H., Xu, Y., Luo, C., Zhu, W., Yu, K., & Jiang, H. (2016). Applying high-performance computing in drug discovery and molecular simulation. National science review, 3(1), 49–63. https://doi.org/10.1093/nsr/nww003

7. Korb O, Finn PW, Jones G. (2014). The cloud and other new computational methods to improve molecular modelling. Expert Opin Drug Dis. 2014;9:1121–31. doi: 10.1517/17460441.2014.941800

8. The COVID-19 High Performance Computing Consortium. Covid-19 HPC Consortium. (n.d.-b). https://covid19-hpc-consortium.org/ 

9. For the First Time, Quantum-Enhanced Generative AI Generates Viable Cancer Drug Candidates. Business Wire. (2024). https://www.businesswire.com/news/home/20240220020899/en/For-the-First-Time-Quantum-Enhanced-Generative-AI-Generates-Viable-Cancer-Drug-Candidates