Rahul Rao in Scientific American: Few computer science breakthroughs have done so much in so little time as the artificial intelligence design known as a transformer. A transformer is a form of deep learning—a machine model based on networks in the brain—that researchers at Google first proposed in 2017. Seven years later the transformer, which enables ChatGPT and other chatbots to quickly generate sophisticated outputs in reply to user prompts, is the dynamo powering the ongoing AI boom. As remarkable as this AI design has already proved to be, what if you could run it on a quantum computer?
That might sound like some breathless mash-up proposed by an excitable tech investor. But quantum-computing researchers are now in fact asking this very question out of sheer curiosity and the relentless desire to make computers do new things. A new study published recently in Quantum used simple hardware to show that rudimentary quantum transformers could indeed work, hinting that more developed quantum-AI combinations might solve crucial problems in areas including encryption and chemistry—at least in theory.
A transformer’s superpower is its ability to discern which parts of its input are more important than others and how strongly those parts connect. Take the sentence “She is eating a green apple.” A transformer could pick out the sentence’s key words: “eating,” “green” and “apple.” Then, based on patterns identified in its training data, it would judge that the action “eating” has little to do with the color “green” but a great deal more to do with the object “apple.” Computer scientists call this feature an “attention mechanism,” meaning it pays the most attention to the most important words in a sentence, pixels in an image or proteins in a sequence. The attention mechanism mimics how humans process language, performing a task that is elementary for most young children but that—until the ChatGPT era—computers had struggled with.
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