Quantum computing and artificial intelligence are both hyped ridiculously. But it seems a combination of the two may indeed combine to open up new possibilities.
In a research paper published today in the journal Nature, researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. The team says that future quantum computers should allow machine learning to hit new levels of complexity.
As first imagined decades ago, quantum computers were seen as a different way to compute information. In principle, by exploiting the strange, probabilistic nature of physics at the quantum, or atomic, scale, these machines should be able to perform certain kinds of calculations at speeds far beyond those possible with any conventional computer (see “What is a quantum computer?”). There is a huge amount of excitement about their potential at the moment, as they are finally on the cusp of reaching a point where they will be practical.
At the same time, because we don’t yet have large quantum computers, it isn’t entirely clear how they will outperform ordinary supercomputers—or, in other words, what they will actually do (see “Quantum computers are finally here. What will we do with them?”).
Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. The resulting machine learning depends on the efficiency and quality of this process. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.
The MIT-IBM researchers performed their simple calculation using a two-qubit quantum computer. Because the machine is so small, it doesn’t prove that bigger quantum computers will have a fundamental advantage over conventional ones, but it suggests that would be the case, The largest quantum computers available today have around 50 qubits, although not all of them can be used for computation because of the need to correct for errors that creep in as a result of the fragile nature of these quantum bits.
“We are still far off from achieving quantum advantage for machine learning,” the IBM researchers, led by Jay Gambetta, write in a blog post. “Yet the feature-mapping methods we’re advancing could soon be able to classify far more complex data sets than anything a classical computer could handle. What we’ve shown is a promising path forward.”
“We’re at stage where we don’t have applications next month or next year, but we are in a very good position to explore the possibilities,” says Xiaodi Wu, an assistant professor at the University of Maryland’s Joint Center for Quantum Information and Computer Science. Wu says he expects practical applications to be discovered within a year or two.
Quantum computing and AI are hot right now. Just a few weeks ago, Xanadu, a quantum computing startup based in Toronto, came up with an almost identical approach to that of the MIT-IBM researchers, which the company posted online. Maria Schuld, a machine-learning researcher at Xanadu, says the recent work may be the start of a flurry of research papers that combine the buzzwords “quantum” and “AI.”
“There is a huge potential,” she says.
A chip design that changes everything: 10 Breakthrough Technologies 2023
Computer chip designs are expensive and hard to license. That’s all about to change thanks to the popular open standard known as RISC-V.
Modern data architectures fuel innovation
More diverse data estates require a new strategy—and the infrastructure to support it.
Chinese chips will keep powering your everyday life
The war over advanced semiconductor technology continues, but China will likely take a more important role in manufacturing legacy chips for common devices.
The computer scientist who hunts for costly bugs in crypto code
Programming errors on the blockchain can mean $100 million lost in the blink of an eye. Ronghui Gu and his company CertiK are trying to help.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.