Brief Description
Our interdisciplinary collaborative team is developing an efficient quantum computing framework to simulate properties of light activated drugs with near-term quantum computers, hence demonstrating the impact of quantum computers to one of the major global challenges in medicine: the treatment of cancer.
Detailed Description
Photodynamic therapy is a safe and effective outpatient cancer treatment with no long-term side effects. The therapy uses a drug, called a photosensitizer, that is activated by light at the specific cancer location. Understanding and accurately simulating light-drug interactions is necessary for the design and selection of drug molecules that maximize the effectiveness of the drug. However, designing optimal light-drug interactions comes with many computational challenges, as the computation time increases exponentially with the size of the drug molecule. Computations are currently carried on high performance computing clusters but cannot provide accurate predictions for larger drug candidates. By contrast, hybrid quantum-classical computing can significantly reduce the computation time while enabling a scale up to larger molecular systems and accelerate discovery of novel drugs.
Our interdisciplinary collaborative team is developing an efficient quantum computing framework to simulate properties of light activated drugs with near-term quantum computers, hence demonstrating the impact of quantum computers to one of the major global challenges in medicine: the treatment of cancer.
Key Highlights
· Value proposition: More effective light activated cancer drugs with minimal side effects
· Benefits/outcomes: An efficient quantum computing framework to simulate properties of light activated drugs with near-term quantum computers
· Advantage of adopting quantum technology, and additionally any advantage v/s classical technology:
Designing optimal light-drug interactions comes with many computational challenges, as the computation time increases exponentially with the size of the drug molecule. Computations are currently carried on high performance computing clusters but cannot provide accurate predictions for larger drug candidates. By contrast, hybrid quantum-classical computing can significantly reduce the computation time while enabling a scale up to larger molecular systems and accelerate discovery of novel drugs.
Publicly Available Technical Data:
