QC Ware Explores Using Quantum Neural Networks to Improve Biomedical Image Analysis with Roche

QC Ware is collaborating with Roche on a research project to optimize image recognition. The project combines the leading expertise of Roche in machine learning applications for biomedical image analysis and that of QC Ware in quantum machine learning and neural networks. The project will explore the potential of quantum neural networks to improve the accuracy, efficiency, and reliability of biomedical image interpretation and analysis. The potential end benefits for patients include earlier detection of diseases and greater diagnostic accuracy.

Biomedical image analysis is a challenging aspect of patient disease detection and diagnosis, and is one of the fastest-growing applications of machine learning in the pharmaceutical industry. However, as the algorithms needed for image analysis become increasingly complex, the research community is looking to quantum computing for better scalability.

QC Ware is designing software to run on near-term quantum computers, and will use publicly available open source datasets to benchmark the potential of quantum computing to improve screening of patients with common blinding retinal diseases, such as age-related macular degeneration and diabetic macular edema. The software could be further adapted to apply to other image analysis datasets for a variety of diseases including breast cancer, lung diseases, and brain tumors.

We believe that this research collaboration with Roche will provide valuable insight into how quantum machine learning in pharma can turn from theory to practice.

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