If you’re read about quantum physics, you know it’s mind-bending stuff. It often defies common sense, so it’s challenging to wrap your head around many of the concepts. Even scientists have been known to joke that no one really understands it.
However, our Stefan Bekiranov, PhD, is putting it to work today to improve tomorrow’s medical research. He and a colleague have developed an algorithm to study genetic diseases using quantum computers, once there are quantum computers capable of running it.
Huh? Stick with me for a minute.
Quantum computers are still in their infancy. But one day soon they may offer computing power on a scale unimaginable using today’s computers.
A little background: Traditional computer programs are built on 1s and 0s. From the computer’s perspective, it’s either-or. 1 or 0. But quantum computers take advantage of a freaky fundamental of quantum physics: Something can be and not be at the same time. So, rather than 1 or 0, a quantum computer’s answer is both, simultaneously. That allows the computer to consider vastly more possibilities at once.
That kind of computing power is super exciting, and it promises to greatly accelerate medical research — especially in data-intense areas such as genetics. So Professor Bekiranov developed his algorithm to be ready to hardness the technology’s potential. And to help advance the field of quantum computing in general.
The challenge is that the technology is … technically demanding, to put it lightly. Many quantum computers have to be kept at near absolute zero — more than 450 degrees below zero Fahrenheit. Even then, the movement of molecules surrounding the quantum computing elements can disrupt the calculations, so algorithms not only have to contain instructions for what to do but for what to do when errors creep in.
“Our goal was to develop a quantum classifier that we could implement on an actual IBM quantum computer,” Professor 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.”
Professor Bekiranov’s 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 (A, G, C or T) for the classification, a conventional computer would execute 3 billion operations to classify the sample. The new quantum algorithm would need only 32.
That will help scientists sort through the vast amount of data required for genetic research. But it’s also proof-of-concept of the usefulness of the technology.
It’s notable that the algorithm has been developed at the School of Medicine. Such algorithms are more likely to emerge from physics or computer science departments. But Bekiranov and collaborator Kunal Kathuria, PhD, were both trained in quantum physics, so they have the particular skills necessary for this cutting-edge work. (Both Bekiranov and Kathuria conducted the study in the School of Medicine’s Department of Biochemistry and Molecular Genetics. Kathuria is currently at the Lieber Institute for Brain Development.)
“Relatively small-scale quantum computers that can solve toy problems are in existence now,” Professor Bekiranov said. “The challenges of developing a powerful universal quantum computer are immense. Along with steady progress, it will take multiple scientific breakthroughs. But time and again, experimental and theoretical physicists, working together, have risen to these challenges. If and when they develop a powerful universal quantum computer, I believe it will revolutionize computation and be regarded as one of greatest scientific and engineering achievements of humankind.”