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Quantum Machine Learning

Diagrammatic Design and Study of Ansätze for Quantum Machine Learning

arXiv
Authors: Richie Yeung

Year

2020

Paper ID

19095

Status

Preprint

Abstract Read

~2 min

Abstract Words

111

Citations

N/A

Abstract

Given the rising popularity of quantum machine learning (QML), it is important to develop techniques that effectively simplify commonly adopted families of parameterised quantum circuits (commonly known as ansätze). This thesis pioneers the use of diagrammatic techniques to reason with QML ansätze. We take commonly used QML ansätze and convert them to diagrammatic form and give a full description of how these gates commute, making the circuits much easier to analyse and simplify. Furthermore, we leverage a combinatorial description of the interaction between CNOTs and phase gadgets to analyse a periodicity phenomenon in layered ansätze and also to simplify a class of circuits commonly used in QML.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • Given the rising popularity of quantum machine learning (QML), it is important to develop techniques that effectively simplify commonly adopted families of parameterised...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2011.11073 [2] arXiv https://arxiv.org/abs/2011.11073 [3] Publisher https://arxiv.org/abs/2011.11073

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