Machine-learning algorithms are helping to unravel the quantum behaviour of a type of superconductor that has baffled physicists for decades.
Researchers used artificial intelligence to spot hidden order in images of a bizarre state in high-temperature superconductors.
The result, published in a pre-print1 on the arXiv earlier last month, supports one theory in a decades-long attempt to understand these materials.
The study also represents the first time that machine learning has been successfully used to make sense of experimental data on quantum matter, said Eun-Ah Kim at Cornell University in Ithaca, New York, who presented the work at the Materials and Mechanisms of Superconductivity and High Temperature Superconductivity meeting in Beijing in August.
In the long term, machine learning could boost efforts to spot simple patterns in other noisy and chaotic experimental systems, such as quantum spin liquids, which could form the basis of a future exotic type of quantum computer.
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