New theoretical research proves that machine learning on quantum computers requires far simpler data than previously believed. The finding paves a path to maximizing the usability of today’s noisy, intermediate-scale quantum computers for simulating quantum systems and other tasks better than classical digital computers, while also offering promise for optimizing quantum sensors.
“We show that surprisingly simple data in a small amount is sufficient to train a quantum neural network,” said Lukasz Cincio, a quantum theorist at Los Alamos National Laboratory. He is a co-author of the paper containing the proof published in the journal Nature Communications. “This work takes another step in the direction of making quantum machine learning easier, more accessible and more near-term.”
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