Diagrammatic Design and Study of Ansätze for Quantum Machine Learning
arXiv:2011.11073
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.
Cited by in corpus (5)
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- Diagrammatic Analysis for Parameterized Quantum Circuits
- Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning
- Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)