Researchers at the University of Virginia School of Medicine are tapping into the potential of quantum computers to understand genetic diseases.
The team developed and implemented a genetic sample classification algorithm, a Hamming distance-like, that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers.
The new algorithm essentially classifies genomic data. It can determine if a test sample comes from a disease or control sample exponentially faster than a conventional computer. For example, if they used all four building blocks of DNA for the classification, a conventional computer would execute 3 billion operations to classify the sample. The new quantum algorithm would need only 32. (GEN)
