Researchers Develop Genetic Sample Classification Algorithm to Study Genetic Diseases Using Quantum Computers

(GeneticEngineeringNews) Researchers at the University of Virginia School of Medicine say they are tapping into the potential of quantum computers to help us understand genetic diseases.
Stefan Bekiranov, PhD, and colleagues have report the development of an algorithm in their new study, “Implementation of a Hamming distance–like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne” published in Quantum Machine Intelligence, to allow researchers to study genetic diseases using quantum computers, once there are much more powerful quantum computers to run it. The algorithm, a complex set of operating instructions, will help advance quantum computing algorithm development and could advance the field of genetic research one day, according to Bekiranov.
“We developed and implemented a genetic sample classification algorithm 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,” Bekiranov said. “This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health.”
“Our goal was to develop a quantum classifier that we could implement on an actual IBM quantum computer. .. We finally found papers from Maria Schuld, PhD, who is a pioneer in developing implementable, near-term, quantum machine learning algorithms. Our classifier builds on those developed by Schuld,” Bekiranov said. “Once we started testing the classifier on the IBM system, we quickly discovered its current limitations and could only implement a vastly oversimplified, or ‘toy,’ problem successfully, for now.”