Quantum Computing Research

Current quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ), have immense power but they are not yet capable of error correction. To overcome this, researchers from the Center for Computational Life Sciences (CCLS) are creating new quantum computing methods to ensure accurate results. These methods can help researchers around the world advance their research leading to better drugs and treatments.

Hybrid models

Hybrid models combine the immense power of quantum computers with the error correction abilities of classical computers. These models are implemented on problems that are inefficiently or inaccurately solved on classical computers. Our researchers are creating and implementing different hybrid models to tackle healthcare’s toughest challenges. Discover how our researchers are applying hybrid models.

Sample-Based Quantum Diagonalization (SQD)

SQD is a quantum-classical hybrid model that our CCLS researchers are currently using to make breakthrough discoveries in chemistry and biomedical research. This method uses quantum computing to take samples of all the possible outcomes for a problem. Classical computing is then used to analyze the samples to determine which ones are the most accurate answer to the problem. Learn how our researchers are using SQD to advance research.

Quantum machine learning

Machine learning (ML) is a type of artificial intelligence (AI) that is capable of learning from data. Current ML methods are limited by the ability of classical computers. CCLS researchers are exploring how quantum computing can enhance ML methods and improve discovery in areas including drug design and discovery, genomics and imaging. Explore how our researchers are implementing quantum machine learning.

Explore more quantum research and accomplishments

• Cleveland Clinic advances in first National Institutes of Health quantum computing challenge