Tensor networks for quantum machine learning
- URL: http://arxiv.org/abs/2303.11735v1
- Date: Tue, 21 Mar 2023 10:46:56 GMT
- Title: Tensor networks for quantum machine learning
- Authors: Hans-Martin Rieser, Frank K\"oster and Arne Peter Raulf
- Abstract summary: We discuss how layouts like MPS, PEPS, TTNs and MERA can be mapped to a quantum computer.
We also discuss how they can be used for machine learning and data encoding and which implementation techniques improve their performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Once developed for quantum theory, tensor networks have been established as a
successful machine learning paradigm. Now, they have been ported back to the
quantum realm in the emerging field of quantum machine learning to assess
problems that classical computers are unable to solve efficiently. Their nature
at the interface between physics and machine learning makes tensor networks
easily deployable on quantum computers. In this review article, we shed light
on one of the major architectures considered to be predestined for variational
quantum machine learning. In particular, we discuss how layouts like MPS, PEPS,
TTNs and MERA can be mapped to a quantum computer, how they can be used for
machine learning and data encoding and which implementation techniques improve
their performance.
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