initial thesis stuff

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Daniel Knüttel 2019-10-11 11:47:17 +02:00
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latex=xelatex
pdflatex=xelatex
bibtex=bibtex
chapters = chapters/introduction.tex \
chapters/naive_simulator.tex
all: main.pdf
main.pdf: main.tex main.bib $(chapters)
$(latex) main
$(bibtex) main
$(latex) main
$(pdflatex) main
clean:
-rm main.aux
-rm main.blg
-rm main.dvi
-rm main.log
-rm main.out
-rm main.pdf
-rm main.toc

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\section{The graph Simulator}
\subsection{Graph Storage}
One of the gread advantages of simulating in the graph formalism is a great increase
in simulation performance and a lower memory requirement. The simulation of
at least $10^6$ qbits on a common desktop computer should be possible\cite{andersbriegel2005}.
Therefore one has to take care when choosing a representation of the graph state.
The following

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\section{Introduction}
--

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\section{The naive Simulator}
A quite big part of the simulations interesting for students and researchers is not
covered by stabilizer states and stabilizer circuits. In particular the
phase estimation algorithm is essential for many applications. Being able to simulate
such an algorithm is essential for education.
\subsection{Core Design}
The core of the simulator are states represented as numpy arrays \cite{numpy_array}
as a both fast, safe and handy storage. They can be modified and viewed without overhead
using python and allow fast modification using so-called NumPy ufuncs\cite{numpy_ufunc}.
All gates are implemented as NumPy ufuncs and map an $N$ qbit simulator state consisting
of a $2^N$ dimensional quantum mechanical state and an $N$ dimensional classical state
to a new $2^N$ dimensional quantum mechanical state and an $N$ dimensional classical state.
A $N$ qbit quantum mechanical state is the outer (kronecker) product of the $N$ single qbuit
state FIXME: source. The

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@online{
numpy,
url={https://numpy.org/},
urldate={19.09.2019},
author={NumPy developers},
title={NumPy},
year=2019
}
@online{
numpy_array,
url={https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html},
urldate={19.09.2019},
author={NumPy developers},
title={numpy.array -- NumPy v1.17 Manual},
year=2019
}
@online{
numpy_ufunc,
url={https://docs.scipy.org/doc/numpy/reference/ufuncs.html},
urldate={19.09.2019},
author={NumPy developers},
title={Universal functions (ufunc) -- NumPy v1.17 Manual},
year=2019
}
@article{
andersbriegel2005,
title={Fast simulation of stabilizer circuits using a graph state representation},
author={Simon Anders and Hans J. Briegel},
year=2005
}

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%\documentclass[a4paper,12pt,oneside]{scrreprt}
\documentclass[a4paper,12pt]{scrartcl}
\usepackage[utf8]{inputenc}
\usepackage{graphicx}
\usepackage{amssymb}
\usepackage{setspace}
\usepackage{amsmath}
\usepackage{hyperref}
\usepackage{geometry}
\usepackage{enumerate}
\geometry{left=2.5cm,right=2.5cm,top=2.5cm,bottom=2.5cm}
\title{Development of an Extensible Quantum Computing
Simulator with a Focus on Simulation in the Graph Formalism }
\author{Daniel Knüttel}
\date{22.09.2019}
\begin{document}
\maketitle
%\frontmatter
\tableofcontents
\section{Acknowledgements}
--
\section{Abstract}
--
%\mainmatter
\include{chapters/introduction}
\include{chapters/naive_simulator}
%\backmatter
\bibliographystyle{unsrt}
\bibliography{main}{}
\end{document}