Scientists at Russia's Skolkovo Institute of Science and Technology (Skoltech) have demonstrated that quantum-enhanced machine learning can be applied to quantum data, surmounting a critical bottleneck.
When applying quantum algorithms to classical data, the information must be stored or otherwise represented by a quantum processor before quantum effects can be employed; this data-readin problem limits the possible acceleration of quantum-enhanced machine learning algorithms.
The Skoltech team combined quantum-enhanced machine learning with quantum-enhanced simulation to explore phase transitions in many-body quantum magnetic problems, in effect training quantum neural networks using only quantum states as data.
This bypasses the data-readin problem by feeding in quantum mechanical states of matter.
Skoltech's Alexey Uvarov said the study is "a step towards understanding the power of quantum devices for machine learning."
From Skolkovo Institute of Science and Technology (Russia)
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA
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