04/25/2024
Cleveland Clinic announces Quantum Innovation Catalyzer Program winners
Cleveland Clinic is excited to announce the inaugural winners of the Quantum Innovation Catalyzer Program, designed to support start-up companies exploring quantum computing applications.
The winners include two Ohio-based companies, Qradle and Picture Health, and Finland-based Algorithmiq. Each winner pitched a quantum project designed to advance critical healthcare and life sciences research.
The 24-week Quantum Innovation Catalyzer Program provides access to IBM's Quantum System One quantum computer to support their research. Winners will connect with Cleveland Clinic experts and working groups to learn and share best practices and new applications.
Other benefits include a graduation and demo day, where companies can present their findings to a curated audience of investors, clinical, corporate and ecosystem partners.
Read more about the companies and their projects below:
- Algorithmiq develops advanced algorithms to solve complex problems in life sciences. The team aims to develop its proprietary software, Aurora, to improve our understanding of photon-drug interactions. By understanding these interactions, researchers can improve cancer treatments, disease prevention and medical imaging.
- Qradle provides quantum software drug discovery solutions. The team plans to develop a group of programs that will work together to aid drug discovery research. This includes a classical-to-quantum computing conversion tool that can leverage existing classical AI/ML solutions for drug discoveries using quantum computing. This work will help investigators advance their work by transitioning their research from classic AI to quantum AI
- Picture Health specializes in cutting-edge AI diagnostics tailored for oncologists. The team will use quantum parallel processing to enhance medical image processing with a focus on digital pathology and high-resolution histology. This will lay the groundwork for cancer researchers to explore novel biomarkers and gain a better understanding of complex biological structures within medical data.