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Better Machine Learning Models with Quantum Computers

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Illustration of a deep learning network with nodes connected by deep links.

Classical and quantum computers can both be used to train machine-learning models, which essentially means solving equations in high-dimensional spaces.

Credit: Daniel Zender

Researchers at European quantum computing company Terra Quantum demonstrated improved training of machine learning models using a method that combines the best features of classical and quantum computers.

The researchers hypothesized that by giving classical and quantum computers the same dataset and allowing them to train models in parallel, the final model combining the two could achieve better results.

Said Terra's Alexey Melnikov, “Quantum is not good for everything, classical is not good for everything, but together they improve each other.”

The researchers used the technique to model gas emissions at a waste-burning thermal power plant.

When they added a quantum neural network layer to an existing classical model, they found the error rate of the model dropped to one-third of what it would have been without quantum.

From IEEE Spectrum
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Abstracts Copyright © 2023 SmithBucklin, Washington, D.C., USA


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