some final work
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@@ -6,6 +6,7 @@
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\usepackage{setspace}
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\usepackage{amsmath}
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\usepackage{hyperref}
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\usepackage{url}
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\usepackage{geometry}
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\usepackage{enumerate}
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\usepackage{physics}
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@@ -13,7 +14,7 @@
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%\usepackage{struktex}
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\usepackage{qcircuit}
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\usepackage{adjustbox}
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\usepackage{tikz}
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%\usepackage{tikz}
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\usetheme{metropolis}
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@@ -71,7 +72,7 @@
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\item{A universal quantum computer is a $2^n$ dimensional complex vector
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to which any unitary can be applied with a quantum mechanical measurement process.}
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\item{Algorithms using the exponentially large Hilbert space can
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solve classically exponentially hard problems in polynomial time.}
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solve some classically exponentially hard problems in polynomial time.}
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\end{itemize}
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\end{frame}
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@@ -275,9 +276,24 @@
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starting from the $\ket{0}^{\otimes n}$ state can be
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simulated and sampled efficiently, i.e. in $\mbox{poly}(n, m)$ time
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where $m$ is the amount of gates/measurements.}
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\end{itemize}
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\end{frame}
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}
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{
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\begin{frame}{The Gottesman-Knill Theorem}
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\begin{itemize}
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\item{Note that a general state has $2^n$ complex coefficients.
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Computing operations on this state is therefore exponentially hard in
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$n$.}
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\item{Note that using the $R_\phi$ is universal and $R_\frac{\pi}{4}$
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allows rational approximations of universal gates.}
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\item{Restricting $\phi$ to $\frac{\pi}{2}$ allows the simulation of
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large numbers of qbits on a classical computer.\\
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{\bf Goal of this project is to create a simulator that can
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simulate several thousand up to millions of qbits.
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}}
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\end{itemize}
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\end{frame}
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@@ -349,13 +365,9 @@
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\begin{itemize}
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\item{In general a Clifford gate $U \in C_n$ will map a stabilizer state to another stabilizer state.
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The new state is stabilized by $\langle U S^{(i)} U^\dagger \rangle_i$.}
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\item{A Pauli observable $g_a \in \{X_a, Y_a, Z_a\}$ will either commute with all stabilizers
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(in this case $g_a$ is a stabilizer, the measurement is deterministic and the stabilizers are unchanged) or
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or anticommute with at least one stabilizer.}
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\item{If $g_a$ anticommutes with a subset $A := \{S^{(i)} | \{S^{(i)}, g_a\} = 0 \}$
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the probability to measure $+1$ or $-1$ is $\frac{1}{2}$ and the stabilizers $A$
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are changed.}
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\item{When going from $+g_a$ to $-g_a$ the results are changed from $+1$ to $-1$ and vice versa.}
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\item{One can show that measurements of Pauli observables are covered by the stabilizer formalism.}
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\item{When measuring Pauli observable probability amplitudes of $0, 1$ or $\frac{1}{2}$ are
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possible.}
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\end{itemize}
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\end{frame}
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}
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@@ -363,14 +375,13 @@
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{
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\begin{frame}{Graphical States}
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\begin{itemize}
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\item{The graphical representation of stabilizer states is an optimized way to write
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\item{The graphical representation is an optimized way to write
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the stabilizers.}
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\item{
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$(V, E, O)$ is called the graphical representation of
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a stabilizer state if $V = \{0, ..., n-1\}$,
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$E \subset \{\{i,j\} | i,j \in V, i \neq j \}$ and
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$O = \{o_0, ..., o_{n-1}\}$ where $o_i \in C_1$.
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$G = (V, E)$ is a graph, $O$ are called vertex operators.
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For a set of vertices $V = \{0, ..., n-1\}$, some edges
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$E \subset V \otimes V$, and local Clifford operators
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$O = \{o_0, ..., o_{n-1}\}$ the tuple $(V, E, O)$
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is the graphical representation of a stabilizer state.
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}
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\item{The state associated with $(V, E, O)$ is given by
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\begin{equation}
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@@ -570,8 +581,20 @@
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{
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\begin{frame}{Implementation}
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\noindent\begin{minipage}{0.5\textwidth}
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\center
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\includegraphics[width=\textwidth,height=0.8\textheight,keepaspectratio=true]{screenshot_github.png}
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\end{minipage} \hfill
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\begin{minipage}{0.4\textwidth}
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{\small
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\begin{itemize}
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\item{The code is GPLv3.0 licensed.}
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\item{GitHub repository: \url{https://github.com/daknuett/pyqcs}}
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\item{Install it via PyPI: \lstinline{pip3 install pyqcs}}
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\item{The code is tested automatically on TravisCI.}
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\end{itemize}
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}
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\end{minipage}
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\end{frame}
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}
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@@ -680,4 +703,10 @@
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\end{itemize}
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\end{frame}
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}
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{
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\begin{frame}{References}
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See the bibliography of my bachelor thesis.
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\end{frame}
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}
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\end{document}
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