Understanding the quantum universe is not an easy thing. Intuitive notions of space and time break down in the tiny realm of subatomic physics, allowing for behavior that seems, to our macro sensibilities, downright weird.

Quantum computers should allow us to harness this strangeness. Such machines could theoretically explore molecular interactions to create new drugs and materials. But perhaps most important, the world itself is built upon this quantum universe — if we want to understand how it works, we probably need quantum tools.

However, current near-term quantum devices are still far from fulfilling that promise, since they can’t reliably execute a large number of quantum interactions. Until researchers can overcome this issue, classical computers remain the best way to solve real-world problems, however inefficiently they do so.

But maybe there’s a workaround, a kind of quantum compromise. A spate of recent papers suggests that it may be possible to take the quantum system you’d like to understand, input its properties into classical machines, and use those machines to predict the quantum system’s behavior. By combining a new way of modeling quantum systems with increasingly sophisticated machine learning algorithms, researchers have established a method for classical machines to model and predict quantum behavior.

“I think the work is very significant,” said Yi-Zhuang You, a physicist at the University of California, San Diego who is unaffiliated with the studies. “It fundamentally changes the field in the sense that it’s the right way to combine quantum computation and machine learning.”

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