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Entanglement (“spooky action”) could help boost quantum machine learning

By IQT News posted 11 Mar 2022

(IEEE.Spectrum) explains that mysterious quantum links could help lead to exponential scale-up for machine learning. Inside Quantum Technology news summarizes.
Scientists have now found the strange quantum phenomenon known as entanglement, which Einstein dubbed “spooky action at a distance,” might help remove a major potential roadblock to implementing quantum machine learning.
One potential application of quantum machine learning is simulating quantum systems—for instance, chemical reactions that might yield insights leading to next-generation batteries or new drugs. This might entail creating models of the molecules of interest, having them interact, and using experiments of how the actual compounds interact as training data to help improve the models.
A machine-learning algorithm’s average performance depends on how much data it has, suggesting the amount of data ultimately limits machine learning’s performance. This raised the possibility that in order to model a quantum system, for example, the amount of training data that a quantum computer might need would grow exponentially as the modeled system became larger. This could potentially eliminate the edge that quantum computing could have over classical computing.
Now scientists have discovered a way to eliminate this exponential overhead. Their findings, verified using quantum-hardware startup Rigetti’s Aspen-4 quantum computer, suggest that adding more entanglement to quantum machine learning can lead to exponential scale-up.
“Trading entanglement for training states could give huge advantages for training certain types of quantum systems,” says study coauthor Andrew Sornborger, a physicist at Los Alamos National Laboratory, in New Mexico.
One potential futuristic application of this work is what the researchers call “black box uploading.” “Wouldn’t it be cool to be able to learn a model of a quantum experiment, then study it on a quantum computer without having to do repeated experiments?” Sornborger says.

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