Skoltech Institute Research Steps Toward Understanding Power of Quantum Devices + Machine Learning

Quantum machines learn ‘quantum data’

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+  Skoltech scientists have shown that quantum enhanced machine learning can be used on quantum (as opposed to classical) data, overcoming a significant slowdown common to these applications and opening a “fertile ground to develop computational insights into quantum systems.”

The lead author of the study, Skoltech doctoral student Alexey Uvarov describes the study as “a step towards understanding the power of quantum devices for machine learning.” Researchers merged an assortment of techniques, which included applying some ideas from tensor networks and entanglement theory in the analysis of their approach.

+  Quantum algorithms have been developed to enhance a range of different computational tasks; more recently this has grown to include quantum enhanced machine learning. Quantum machine learning was partly pioneered by Skoltech’s resident-based Laboratory for Quantum Information Processing, led by Jacob Biamonte, a coathor of this paper.

+  “Machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is not surprising that quantum computers might outperform classical computers on machine learning tasks,” he says.

+  The standard approach to quantum enhanced machine learning has been to apply quantum algorithms to classical data. In other words, classical data (represented by bit strings of 1’s and 0’s) must be stored or otherwise represented by a quantum processor before quantum effects can be utilized. This is called the data-readin problem. Data-readin serves to limit the speedup that is possible using quantum enhanced machine learning algorithms.

+  The authors circumvent the data-readin problem by feeding in quantum mechanical states of matter.

Source:  Skolkovo Institute of Science and Technology,  Quantum machines learn ‘quantum data’…

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