Last week, Google sparked controversy in the scientific community by claiming that it has achieved the anticipated milestone known as quantum supremacy. In a paper published in Nature, Google described the experiments conducted on a new quantum machine, code named Sycamore, which prooved the famous benchmark. It only took a few hours for IBM, Google’s archrival in the race toward quantum dominance, to publish a paperrefuting Google’s claims, sparking a passionate debate within the computer science community.
Despite the controversy surrounding Google’s claims, there is no doubt that the release of Sycamore represents a major milestone to demonstrate the viability of quantum systems and that it has profound ramifications across other technology fields. In the case of artificial intelligence(AI), there has been a lot of speculation in terms of how the advent of quantum computing will affect AI programs. However, not many people think about how AI can influence the development of quantum computing.
What is Quantum Supremacy?
The term quantum supremacy was originally coined in 2012 by John Preskill, a theoretical physicist at the California Institute of Technology. The term was a generic definition to describe the point where quantum computers could do things unachievable by classical computers. The term was immediately embraced by the quantum community, but different experts developed different theories of what it practically meant.
The controversy surrounding the quantum supremacy term has to do with practicality of certain computations. Given enough time, classical computers can solve the same problems of quantum computers. However, the time for those calculations might be unpractical for any real world problem. In the case of Google, its paper claims that the Sycamore processor took 200 seconds to perform a calculation that the world’s best supercomputer—which happens to be IBM’s Summit machine—would need 10,000 years to match. That time doesn’t seem very practical. However, IBM claims that their supercomputer can solve the same puzzle in 2.5 days, which seems a bit more practical for some tasks.