initial thesis stuff

thesis/chapters/graph_simulator.tex

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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 

thesis/chapters/introduction.tex

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+\section{Introduction}
+
+--

thesis/chapters/naive_simulator.tex

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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 
+
+