diff --git a/src/thesis/chapters/2_fundamentals.tex b/src/thesis/chapters/2_fundamentals.tex index 660a101..b9809a6 100644 --- a/src/thesis/chapters/2_fundamentals.tex +++ b/src/thesis/chapters/2_fundamentals.tex @@ -224,25 +224,25 @@ construction for the [7,4,3]-Hamming code. } \begin{tikzpicture} - \node[VN, label=above:$x_1$] (vn1) {}; - \node[VN, right=12mm of vn1, label=above:$x_2$] (vn2) {}; - \node[VN, right=12mm of vn2, label=above:$x_3$] (vn3) {}; - \node[VN, right=12mm of vn3, label=above:$x_4$] (vn4) {}; - \node[VN, right=12mm of vn4, label=above:$x_5$] (vn5) {}; - \node[VN, right=12mm of vn5, label=above:$x_6$] (vn6) {}; - \node[VN, right=12mm of vn6, label=above:$x_7$] (vn7) {}; + \node[VN, label=above:$x_0$] (vn1) {}; + \node[VN, right=12mm of vn1, label=above:$x_1$] (vn2) {}; + \node[VN, right=12mm of vn2, label=above:$x_2$] (vn3) {}; + \node[VN, right=12mm of vn3, label=above:$x_3$] (vn4) {}; + \node[VN, right=12mm of vn4, label=above:$x_4$] (vn5) {}; + \node[VN, right=12mm of vn5, label=above:$x_5$] (vn6) {}; + \node[VN, right=12mm of vn6, label=above:$x_6$] (vn7) {}; \node[ CN, below=25mm of vn4, - label={below:$x_1 + x_3 + x_4 + x_6 = 0$} + label={below:$x_0 + x_2 + x_3 + x_5 = 0$} ] (cn2) {}; \node[ CN, left=40mm of cn2, - label={below:$x_2 + x_3 + x_4 + x_5 = 0$} + label={below:$x_1 + x_2 + x_3 + x_4 = 0$} ] (cn1) {}; \node[ CN, right=40mm of cn2, - label={below:$x_1 + x_2 + x_4 + x_7 = 0$} + label={below:$x_0 + x_1 + x_3 + x_6 = 0$} ] (cn3) {}; \foreach \n in {2,3,4,5} { @@ -268,9 +268,9 @@ construction for the [7,4,3]-Hamming code. % Mathematically, we represent a \ac{vn} using the index $i \in -\mathcal{I} := \left[ -1 : n \right]$ and a \ac{cn} using the index $j \in \mathcal{J} -:= \left[ 1 : m \right]$. +\mathcal{I} := \left[ 0:n-1 \right] := \left\{ 0,1,\ldots,n-1 \right\}$ +and a \ac{cn} using the index $j \in \mathcal{J} +:= \left[ 0 : m-1 \right]$. We can then encode the information contained in the graph by defining the neighborhood of a variable node $i$ as $\mathcal{N}_\text{V} (i) = \left\{ j \in \mathcal{J} : \bm{H}_{j,i} diff --git a/src/thesis/chapters/3_fault_tolerant_qec.tex b/src/thesis/chapters/3_fault_tolerant_qec.tex index f0b5fea..71fee32 100644 --- a/src/thesis/chapters/3_fault_tolerant_qec.tex +++ b/src/thesis/chapters/3_fault_tolerant_qec.tex @@ -41,7 +41,7 @@ address both. % Definition of fault tolerance We model the possible occurrence of errors during any processing -stage as different \emph{error locations} $E_i,~i\in \{1,\ldots,N\}$ +stage as different \emph{error locations} $E_i,~i\in [1:N]$ in the circuit. $N \in \mathbb{N}$ is the total number of considered error locations. The \emph{circuit error vector} $\bm{e} \in \{0,1\}^N$ is a vector @@ -861,7 +861,7 @@ can add the results from the previous round, as illustrated in \Cref{fig:detectors_from_measurements_general}. We thus have $D=n-k$. Concretely, we denote the outcome of -measurement $\ell \in \{1,\ldots,n-k\}$ in round $r \in \{1,\ldots,R\}$ by +measurement $\ell \in [1:n-k]$ in round $r \in [1:R]$ by $m_\ell^{(r)} \in \mathbb{F}_2$ and define \begin{gather*} @@ -873,7 +873,7 @@ and define \end{pmatrix} .% \end{gather*} -Similarly, we denote the outcome of detector $j\in\{1,\ldots,D\}$ in +Similarly, we denote the outcome of detector $j\in[1:D]$ in round $r$ by $d_j^{(r)} \in \mathbb{F}_2$ and define \begin{gather} \label{eq:measurement_combination} diff --git a/src/thesis/chapters/4_decoding_under_dems.tex b/src/thesis/chapters/4_decoding_under_dems.tex index 07b45c7..9574ae6 100644 --- a/src/thesis/chapters/4_decoding_under_dems.tex +++ b/src/thesis/chapters/4_decoding_under_dems.tex @@ -474,9 +474,8 @@ and is difficult to predict beforehand. We briefly reintroduce the notation important for the definition of the windows. We use the variables $n,m \in \mathbb{N}$ to describe the number of \acp{vn} and \acp{cn} respectively. -We index the \acp{vn} using the variable $i \in \mathcal{I} := \left\{ -1,\ldots,n \right\}$ and the \acp{cn} using the variable $j \in -\mathcal{J} := \left\{ 1, \ldots, m \right\}$. +We index the \acp{vn} using the variable $i \in \mathcal{I} := +[0:n-1]$ and the \acp{cn} using the variable $j \in \mathcal{J} := [ 0 : m-1]$. Finally, we call $\mathcal{N}_\text{V}(i) = \left\{ i\in \mathcal{I}: \bm{H}_{j,i} = 1 \right\}$ and $\mathcal{N}_\text{C}(j) := \left\{ j \in \mathcal{J} : \bm{H}_{j,i} = 1 \right\}$ the neighborhoods of the @@ -487,9 +486,8 @@ check matrix of the underlying code, from which the \ac{dem} was generated. % How we get the corresponding rows We begin by describing the sets of \acp{cn} relevant to each window. -For indexing, we use the variable $\ell \in \left\{ -0,\ldots,n_\text{win} - 1 \right\}$, where $n_\text{win} \in \mathbb{N}$ -is the number of windows. +For indexing, we use the variable $\ell \in [0:n_\text{win} - 1]$, +where $n_\text{win} \in \mathbb{N}$ is the number of windows. Because we defined the step size $F$ as the number of syndrome extraction rounds to skip, the first \ac{cn} of window $\ell$ should have index $\ell F m$.