by Amara Graps
Cancer Use Cases
When it comes to social impact, tackling cancer is one of the most pressing challenges in human health today, especially in the context of using quantum technology. The quest to solve cancer has been a multidisciplinary grand challenge for many years, attracting attention from the High-Performance Computing (HPC) community as early as 1992 in the USA. Organizations like the Cancer Grand Challenges from Cancer Research UK and the National Cancer Institute in the US have taken this challenge to an international level.
Last year, the Welcome Leap initiative recognized the potential of quantum technology in addressing cancer-related issues. One such project by a team from Infleqtion, the University of Chicago, and MIT aims to use artificial intelligence (AI) and quantum algorithms for personalized diagnostics and treatments in cancer. This intersection of AI, quantum computing, and oncology holds promise for groundbreaking developments in cancer research.
Cancer Applications: Broadly
A recent post by Tomesh et al. delves into the co-design of hybrid quantum-classical applications in multimodal cancer research, emphasizing the importance of collaboration across disciplines to fully leverage quantum computing’s potential in oncology. Their work, as published in Nature, explores various applications ranging from molecular modeling and genomics to quantum imaging and sensors, highlighting the methods and techniques involved in each domain.
Table 1 – Quantum Computing Approaches and Advantages for Oncology Applications
Quantum sensors, in particular, play a crucial role in medical diagnostics, offering enhanced sensitivity and resolution for imaging purposes. These sensors, when integrated with quantum processors, enable real-time analysis and insights into disease states, making them valuable tools in cancer research.
The team’s approach to leveraging quantum technology for cancer research involves a broad view that encompasses molecular modeling, genomics, and quantum imaging. By developing end-to-end hybrid applications that combine classical and quantum computing, oncologists can address key processing bottlenecks and optimize the use of quantum accelerators in their work.
Cancer Applications: Specifically
Focusing on the discovery of biomarkers in multimodal cancer data, the team aims to identify biomarkers that can provide valuable insights into cancer progression and treatment outcomes. Their work involves sophisticated computational techniques to navigate the complexities of large and diverse datasets, highlighting the significance of feature selection and optimization in machine learning models.
By utilizing hybrid quantum-classical feature selection algorithms, the team aims to streamline data analysis processes and enhance the performance of machine learning models in cancer research. These early research findings showcase the potential of quantum computing in driving advancements in cancer diagnostics and personalized treatments.
As the field of quantum computing continues to evolve, the integration of quantum technologies in cancer research holds immense promise for revolutionizing the way we understand and treat this complex disease. With ongoing research and collaborations across disciplines, quantum solutions for oncology are poised to reshape the landscape of cancer treatment in the years to come.
(*) For those interested in exploring quantum sensors in medical diagnostics and other quantum use cases, organizations like GQI offer interactive resources and insights to delve deeper into the world of quantum technologies.
Published: September 4, 2024