Add rest of BPGD results

This commit is contained in:
2026-04-17 10:26:20 +02:00
parent 817859d09e
commit 24c574d120
56 changed files with 1091 additions and 122 deletions

View File

@@ -21,76 +21,31 @@ df.to_csv('${filename}', index=False)
i=1
sp="/-\|"
echo "Copying BP param exploration results..."
echo -n ' '
for decoder in "WindowingSyndromeMinSumDecoder" "WindowingSyndromeSpaDecoder"; do
for max_iter in 32 200 5000; do
for pass_soft_info in "True" "False"; do
for F in 1 2 3; do
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}+rust_exp=soft_v_hard_bp,decoder.class_name=${decoder},decoder.max_iter=${max_iter},decoder.pass_soft_info=${pass_soft_info},system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/WF/${decoder}/max_iter_${max_iter}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}/"
mkdir -p ${DEST_DIR}
DEST_FILE="${DEST_DIR}/LERs.csv"
cp ${SRC_FILE} ${DEST_FILE}
post_process_LERs ${DEST_FILE}
printf "\b${sp:i++%${#sp}:1}"
done
done
done
done
done
# Copy BPGD param exploration results
echo -e "\rCopying BPGD param exploration results..."
echo -n ' '
for max_iter in 32 200 5000; do
for pass_soft_info in "True" "False"; do
for F in 1 2 3; do
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}/+rust_exp=soft_v_hard_bpgd,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.max_iter=${max_iter},decoder.pass_soft_info=${pass_soft_info},system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_${max_iter}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}/"
mkdir -p ${DEST_DIR}
DEST_FILE="${DEST_DIR}/LERs.csv"
cp ${SRC_FILE} ${DEST_FILE}
post_process_LERs ${DEST_FILE}
printf "\b${sp:i++%${#sp}:1}"
done
done
done
done
# Copy BP over max iter. results
echo "\rCopying BP over max. iter. results..."
echo -n ' '
for decoder in "WindowingSyndromeMinSumDecoder" "WindowingSyndromeSpaDecoder"; do
for p in 0.001 0.0025 0.004; do
for pass_soft_info in "True" "False"; do
for F in 1 2 3; do
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bp,decoder.class_name=${decoder},decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=${p},system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/max_iter/${decoder}/p_${p}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
mkdir -p ${DEST_DIR}
DEST_FILE="${DEST_DIR}/LERs.csv"
cp ${SRC_FILE} ${DEST_FILE}
post_process_LERs ${DEST_FILE}
printf "\b${sp:i++%${#sp}:1}"
done
done
done
done
done
# Copy BPGD over max iter. results
# echo -e "\rCopying BPGD over max. iter. results..."
# echo "Copying BP param exploration results..."
# echo -n ' '
# for decoder in "WindowingSyndromeMinSumDecoder" "WindowingSyndromeSpaDecoder"; do
# for max_iter in 32 200 5000; do
# for pass_soft_info in "True" "False"; do
# for F in 1 2 3; do
# for W in 3 4 5; do
# SRC_PATH="${BASE_PATH}+rust_exp=soft_v_hard_bp,decoder.class_name=${decoder},decoder.max_iter=${max_iter},decoder.pass_soft_info=${pass_soft_info},system.F=${F},system.W=${W}/"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/WF/${decoder}/max_iter_${max_iter}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}/"
# mkdir -p ${DEST_DIR}
# DEST_FILE="${DEST_DIR}/LERs.csv"
# cp ${SRC_FILE} ${DEST_FILE}
# post_process_LERs ${DEST_FILE}
# printf "\b${sp:i++%${#sp}:1}"
# done
# done
# done
# done
# done
#
# # Copy BPGD param exploration results
#
# echo -e "\rCopying BPGD param exploration results..."
# echo -n ' '
# for max_iter in 32 200 5000; do
# for pass_soft_info in "True" "False"; do
@@ -99,7 +54,7 @@ done
# SRC_PATH="${BASE_PATH}/+rust_exp=soft_v_hard_bpgd,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.max_iter=${max_iter},decoder.pass_soft_info=${pass_soft_info},system.F=${F},system.W=${W}/"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoder/max_iter_${max_iter}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}/"
# DEST_DIR="res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_${max_iter}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}/"
# mkdir -p ${DEST_DIR}
# DEST_FILE="${DEST_DIR}/LERs.csv"
# cp ${SRC_FILE} ${DEST_FILE}
@@ -109,3 +64,48 @@ done
# done
# done
# done
#
# # Copy BP over max iter. results
#
# echo -e "\rCopying BP over max. iter. results..."
# echo -n ' '
# for decoder in "WindowingSyndromeMinSumDecoder" "WindowingSyndromeSpaDecoder"; do
# for p in 0.001 0.0025 0.004; do
# for pass_soft_info in "True" "False"; do
# for F in 1 2 3; do
# for W in 3 4 5; do
# SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bp,decoder.class_name=${decoder},decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=${p},system.F=${F},system.W=${W}/"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/max_iter/${decoder}/p_${p}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
# mkdir -p ${DEST_DIR}
# DEST_FILE="${DEST_DIR}/LERs.csv"
# cp ${SRC_FILE} ${DEST_FILE}
# post_process_LERs ${DEST_FILE}
# printf "\b${sp:i++%${#sp}:1}"
# done
# done
# done
# done
# done
# Copy BPGD over max iter. results
echo -e "\rCopying BPGD over max. iter. results..."
echo -n ' '
for p in 0.001 0.0025 0.004; do
for pass_soft_info in "True" "False"; do
for F in 1 2 3; do
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=${p},system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_${p}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
mkdir -p ${DEST_DIR}
DEST_FILE="${DEST_DIR}/LERs.csv"
cp ${SRC_FILE} ${DEST_FILE}
post_process_LERs ${DEST_FILE}
printf "\b${sp:i++%${#sp}:1}"
done
done
done
done

View File

@@ -51,6 +51,8 @@
\usepackage{nicematrix}
\usepackage{xpatch}
\usepackage{lmodern}
\usepackage{algorithmic}
\usepackage{algorithm}
\title{Fault Tolerant Quantum Error Correction}
\subtitle{Master's Thesis Midterm Presentation}
@@ -117,6 +119,11 @@
%
%
\DeclareAcronym{sced}{
short=SCED,
long=sub-code ensemble decoding
}
\DeclareAcronym{qec}{
short=QEC,
long=quantum error correction
@@ -157,6 +164,11 @@
long=guided decimation
}
\DeclareAcronym{gdg}{
short=GDG,
long=guided decimation guessing
}
\DeclareAcronym{osd}{
short=OSD,
long=ordered statistics decoding,
@@ -2222,7 +2234,7 @@
]
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
@@ -2238,7 +2250,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
@@ -2300,7 +2312,7 @@
]
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, solid, gray, opacity=0.4]
@@ -2325,11 +2337,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
@@ -2345,7 +2353,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
@@ -2407,7 +2415,7 @@
]
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, solid, gray, opacity=0.4]
@@ -2432,7 +2440,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
@@ -2448,7 +2456,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
@@ -2523,7 +2531,7 @@
]
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, forget plot, \col]
@@ -2537,7 +2545,7 @@
}
\foreach \F/\col in
{3/kit-orange,2/kit-blue,1/kit-red} {
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
@@ -2570,16 +2578,55 @@
\end{minipage}
\end{frame}
\begin{frame}
\begin{frame}[fragile]
\frametitle{BP with Guided Decimation}
\begin{itemize}
\item \ac{gd}
\item After every $T$ \ac{bp} iterations, fix most reliable
variable node
\end{itemize}
% \begin{minipage}{0.65\textwidth}
% \centering
% \begin{algorithm}[H]
% \caption{BP with guided decimation (BPGD)
% \citereferencemanual{YLH+24}}
% \begin{algorithmic}[1]
% \STATE \textbf{for} $r=1$ \textbf{to} $n$ \textbf{do}
% \STATE \hspace{5mm} Run $T$ BP iterations
% \STATE $\hat{x} \leftarrow$
% \STATE \textbf{done}
% \end{algorithmic}
% \end{algorithm}
% \end{minipage}%
% \begin{minipage}{0.35\textwidth}
% \centering
% \begin{itemize}
% \item asdf
% \end{itemize}
% \end{minipage}
\vspace*{15mm}
\addreferencesmanual
{YLH+24}{Hanwen Yao et al. ``Belief propagation decoding of quantum
LDPC codes with guided decimation,'' 2024 IEEE International
Symposium on Information Theory (ISIT), 2024.}
\stopreferencesendmanual
\end{frame}
\begin{frame}
\frametitle{BPGD Performance Investigation I}
\frametitle{BPGD Performance: Window Size I}
\vspace*{-18mm}
\begin{itemize}
\item Examine decoding performance for \schlagwort{BPGD}
\item Compare cold- and warm-start decoding
\end{itemize}
\vspace*{5mm}
\only<1>{
\begin{minipage}{0.66\textwidth}
@@ -2906,7 +2953,7 @@
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $n_\text{iterations} = 5000$
\item $n_\text{iterations} = n $
\item $F = 1$
\end{itemize}
\end{itemize}
@@ -2915,7 +2962,481 @@
\end{frame}
\begin{frame}
\frametitle{BPGD Performance Investigation II}
\frametitle{BPGD Performance: Window Size II}
\vspace*{-18mm}
\begin{itemize}
\item Examine decoding performance for \schlagwort{BPGD}
\item Compare cold- and warm-start decoding
\end{itemize}
\begin{minipage}{0.66\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
width=16cm,
height=12cm,
ymode=log,
% xmode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-3, ymax=1e-1,
grid=both,
legend pos = north east,
xtick={32,512,1024,2048,4096},
% xtick={0.001,0.0015,...,0.004},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
xlabel={Number of \ac{bp} iterations},
ylabel={Per-round-LER},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=15mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \W/\col in
{3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, forget plot, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_0.0025/pass_soft_info_False/F_1/W_\W/LERs.csv};
}
\temp
}
\foreach \W/\col in
{3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_0.0025/pass_soft_info_True/F_1/W_\W/LERs.csv};
}
\temp
\addlegendentryexpanded{$W = \W$}
}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
\centering
\begin{itemize}
\item $[[144,12,12]]$-\ac{bb} code, \\
$12$ \ac{se} rounds
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $p = 0.0025$
\item $F = 1$
\end{itemize}
\end{itemize}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{BPGD Performance: Step Size I}
\vspace*{-18mm}
\begin{itemize}
\item Examine decoding performance for \schlagwort{BPGD}
\item Compare cold- and warm-start decoding
\end{itemize}
\vspace*{5mm}
\only<1> {
\begin{minipage}{0.66\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
width=16cm,
height=12cm,
ymode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-5, ymax=2e-1,
grid=both,
legend pos = south east,
xtick={0.001,0.0015,...,0.004},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
xlabel={Physical error rate},
ylabel={Per-round-LER},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=15mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_32/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\addlegendentryexpanded{$F = \F$}
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_32/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
\centering
\begin{itemize}
\item $[[144,12,12]]$-\ac{bb} code, \\
$12$ \ac{se} rounds
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $n_\text{iterations} = 32$
\item $W = 5$
\end{itemize}
\end{itemize}
\end{minipage}
}
\only<2> {
\begin{minipage}{0.66\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
width=16cm,
height=12cm,
ymode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-5, ymax=2e-1,
grid=both,
legend pos = south east,
xtick={0.001,0.0015,...,0.004},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
xlabel={Physical error rate},
ylabel={Per-round-LER},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=15mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, solid, gray, opacity=0.4]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_32/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_32/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_200/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\addlegendentryexpanded{$F = \F$}
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_200/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
\centering
\begin{itemize}
\item $[[144,12,12]]$-\ac{bb} code, \\
$12$ \ac{se} rounds
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $n_\text{iterations} = 200$
\item $W = 5$
\end{itemize}
\end{itemize}
\end{minipage}
}
\only<3> {
\begin{minipage}{0.66\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
width=16cm,
height=12cm,
ymode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-5, ymax=2e-1,
grid=both,
legend pos = south east,
xtick={0.001,0.0015,...,0.004},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
xlabel={Physical error rate},
ylabel={Per-round-LER},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=15mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, solid, gray, opacity=0.4]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_200/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_200/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_5000/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\addlegendentryexpanded{$F = \F$}
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, \col]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/WF/WindowingSyndromeSpaGdDecoder/max_iter_5000/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
\centering
\begin{itemize}
\item $[[144,12,12]]$-\ac{bb} code, \\
$12$ \ac{se} rounds
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $n_\text{iterations} = 5000$
\item $W = 5$
\end{itemize}
\end{itemize}
\end{minipage}
}
\end{frame}
\begin{frame}
\frametitle{BPGD Performance: Step Size II}
\vspace*{-18mm}
\begin{itemize}
\item Examine decoding performance for \schlagwort{BPGD}
\item Compare cold- and warm-start decoding
\end{itemize}
\begin{minipage}{0.66\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}
\begin{axis}[
width=16cm,
height=12cm,
ymode=log,
% xmode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-3, ymax=1e-1,
grid=both,
legend pos = north east,
xtick={32,512,1024,2048,4096},
% xtick={0.001,0.0015,...,0.004},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
scaled x ticks=false,
xlabel={Number of \ac{bp} iterations},
ylabel={Per-round-LER},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=15mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
densely dashed, forget plot, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_0.0025/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\foreach \F/\col in
{3/kit-red,2/kit-blue,1/kit-orange} {
\edef\temp{\noexpand
\addplot+[mark=o, line width=2pt,
solid, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_0.0025/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\addlegendentryexpanded{$F = \F$}
}
\end{axis}
\end{tikzpicture}
\end{figure}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
\centering
\begin{itemize}
\item $[[144,12,12]]$-\ac{bb} code, \\
$12$ \ac{se} rounds
\item \ac{spa} + \ac{gd} decoder
\item Parameters
\begin{itemize}
\item $p = 0.0025$
\item $W = 5$
\end{itemize}
\end{itemize}
\end{minipage}
\end{frame}
% \begin{frame}
@@ -2977,49 +3498,65 @@
\begin{frame}
\frametitle{Conclusion and Outlook}
\vspace*{-10mm}
\vspace*{-7mm}
\begin{minipage}[c]{0.65\textwidth}
\begin{itemize}
\item Problem setting
\item Conclusion
\begin{itemize}
\item Research area: Decoder design for \acp{dem}
under circuit-level noise
\item Research gap: Consideration of \acp{dem} as
\ac{scldpc} codes
\end{itemize}
\vspace*{5mm}
\item Future work
\begin{itemize}
\item Modify existing decoder to pass soft information
\item Test different \ac{bp} variations
\item \ldots
\end{itemize}
\vspace*{5mm}
\item Parameters
\begin{itemize}
\item Use standard depolarizing noise for comparability
\item Compare performance with other \ac{bb}
code decoders
\item Use soft information for warm start \\
$\rightarrow$ \schlagwort{More effective
iterations} with no additional\\
\hspace*{8mm} overhead
\end{itemize}
\visible<2->{
\item Future work
\begin{itemize}
\item Incorporate the sliding-window structure in \\
\schlagwort{decimation strategy} of BPGD
\item Examine other \schlagwort{inner
decoders} (e.g., \\
guided decimation guesssing
\citereferencemanual{GCR24},
neural BP4 \citereferencemanual{MSL$^+$25})
\item Exploit spatially-coupled structure for
\schlagwort{code design} (e.g., increase
coupling width)
\end{itemize}
}
\end{itemize}
\end{minipage}%
\begin{minipage}[c]{0.35\textwidth}
\centering
\pause
\begin{figure}[H]
\centering
\visible<3->{
\begin{figure}[H]
\centering
\vspace*{-25mm}
\begin{tikzpicture}
\node[scale=10] at (0, 0)
{\textcolor{kit-blue}{{\fontfamily{phv}\selectfont ?}}};
\vspace*{-25mm}
\begin{tikzpicture}
\node[scale=10] at (0, 0)
{\textcolor{kit-blue}{{\fontfamily{phv}\selectfont ?}}};
\node[align=center] at (0,-5) {Thank you for your
attention! \\ Any questions?};
\end{tikzpicture}
\end{figure}
\node[align=center] at (0,-5) {Thank you for your
attention! \\ Any questions?};
\end{tikzpicture}
\end{figure}
}
\end{minipage}
\vspace*{15mm}
\addreferencesmanual
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward
Low-latency Iterative Decoding of QLDPC Codes Under
Circuit-Level Noise,'', 2024.
}
{MSL$^+$25}{
S. Miao et al., ``Quaternary Neural Belief Prop.
Decoding of Quantum LDPC Codes with Overcomplete
Check Matrices'', \emph{IEEE Access}, 2025.
}
\stopreferencesmanual
\end{frame}
\appendix
@@ -3207,7 +3744,7 @@
\begin{minipage}{0.57\textwidth}
\begin{itemize}
\item BP guided decimation (BPGD) \\
\item BP with guided decimation (BPGD) \\
$\rightarrow$ Iteratively fix most reliable
variable node (VN)
\vspace*{10mm}

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0900455927051671,0.007832562908884788,473.9999999999996
128,5264,0.0433130699088145,0.0036831237526303573,227.99999999999952
256,10528,0.0373290273556231,0.0031652794634116077,393.0
512,10528,0.0343844984802431,0.0029115494911087225,361.9999999999994
1024,10528,0.031724924012158,0.002682982906081044,333.99999999999943
2048,52633,0.003932893811867,0.0003283334181387598,206.9999999999958
4096,100000,0.00041,3.417308883513215e-05,41.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0900455927051671 0.007832562908884788 473.9999999999996
3 128 5264 0.0433130699088145 0.0036831237526303573 227.99999999999952
4 256 10528 0.0373290273556231 0.0031652794634116077 393.0
5 512 10528 0.0343844984802431 0.0029115494911087225 361.9999999999994
6 1024 10528 0.031724924012158 0.002682982906081044 333.99999999999943
7 2048 52633 0.003932893811867 0.0003283334181387598 206.9999999999958
8 4096 100000 0.00041 3.417308883513215e-05 41.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0769376899696048,0.006649339536470622,404.99999999999966
128,10528,0.0289703647416413,0.0024468591153834485,304.9999999999996
256,10528,0.0222264437689969,0.0018713446265007372,233.99999999999935
512,10528,0.0203267477203647,0.0017098847247569715,213.99999999999957
1024,15792,0.019756838905775,0.0016615027176706265,311.9999999999988
2048,15792,0.0151342451874366,0.0012700208923792644,238.99999999999878
4096,100000,0.0002,1.6668194639746226e-05,20.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0769376899696048 0.006649339536470622 404.99999999999966
3 128 10528 0.0289703647416413 0.0024468591153834485 304.9999999999996
4 256 10528 0.0222264437689969 0.0018713446265007372 233.99999999999935
5 512 10528 0.0203267477203647 0.0017098847247569715 213.99999999999957
6 1024 15792 0.019756838905775 0.0016615027176706265 311.9999999999988
7 2048 15792 0.0151342451874366 0.0012700208923792644 238.99999999999878
8 4096 100000 0.0002 1.6668194639746226e-05 20.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0721884498480243,0.006224433929583384,379.99999999999994
128,10528,0.0246960486322188,0.002081672938477608,259.99999999999955
256,10528,0.0205167173252279,0.0017260177938905885,215.99999999999935
512,15792,0.0194402228976697,0.0016346349653756365,306.9999999999999
1024,15792,0.0191869300911854,0.0016131464887009228,302.99999999999983
2048,15792,0.0183637284701114,0.0015433440537602205,289.99999999999926
4096,100000,0.00015,1.250085945736501e-05,14.999999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0721884498480243 0.006224433929583384 379.99999999999994
3 128 10528 0.0246960486322188 0.002081672938477608 259.99999999999955
4 256 10528 0.0205167173252279 0.0017260177938905885 215.99999999999935
5 512 15792 0.0194402228976697 0.0016346349653756365 306.9999999999999
6 1024 15792 0.0191869300911854 0.0016131464887009228 302.99999999999983
7 2048 15792 0.0183637284701114 0.0015433440537602205 289.99999999999926
8 4096 100000 0.00015 1.250085945736501e-05 14.999999999999998

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.1508358662613981,0.013532828789684093,793.9999999999997
128,5264,0.1082826747720364,0.009505046788669924,569.9999999999997
256,5264,0.1031534954407294,0.00903151467250718,542.9999999999995
512,5264,0.104483282674772,0.00915404340966608,549.9999999999998
1024,5264,0.092515197568389,0.0080572366865439,486.99999999999966
2048,10528,0.0243161094224924,0.0020492829827954973,256.0
4096,89474,0.0022352862284015,0.0001864649640629379,199.9999999999958
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.1508358662613981 0.013532828789684093 793.9999999999997
3 128 5264 0.1082826747720364 0.009505046788669924 569.9999999999997
4 256 5264 0.1031534954407294 0.00903151467250718 542.9999999999995
5 512 5264 0.104483282674772 0.00915404340966608 549.9999999999998
6 1024 5264 0.092515197568389 0.0080572366865439 486.99999999999966
7 2048 10528 0.0243161094224924 0.0020492829827954973 256.0
8 4096 89474 0.0022352862284015 0.0001864649640629379 199.9999999999958

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0767477203647416,0.006632304876161399,403.9999999999998
128,10528,0.0348594224924012,0.0029524256669828386,366.99999999999983
256,10528,0.0316299392097264,0.002674830462000899,332.99999999999955
512,10528,0.0291603343465045,0.0024631237898063985,306.9999999999994
1024,10528,0.027830547112462,0.002349332278163696,292.99999999999994
2048,10528,0.024411094224924,0.002057379387650271,256.9999999999999
4096,100000,0.00034,2.8337749570450654e-05,34.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0767477203647416 0.006632304876161399 403.9999999999998
3 128 10528 0.0348594224924012 0.0029524256669828386 366.99999999999983
4 256 10528 0.0316299392097264 0.002674830462000899 332.99999999999955
5 512 10528 0.0291603343465045 0.0024631237898063985 306.9999999999994
6 1024 10528 0.027830547112462 0.002349332278163696 292.99999999999994
7 2048 10528 0.024411094224924 0.002057379387650271 256.9999999999999
8 4096 100000 0.00034 2.8337749570450654e-05 34.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0699088145896656,0.00602118695926479,367.99999999999966
128,10528,0.0274506079027355,0.002316846624060842,288.9999999999993
256,10528,0.0254559270516717,0.0021464875636995062,267.99999999999966
512,10528,0.0227963525835866,0.0019198386716094973,239.99999999999974
1024,10528,0.0211816109422492,0.0017825061322593871,222.99999999999957
2048,10528,0.0196618541033434,0.0016534415568043581,206.99999999999932
4096,100000,0.00025,2.083572086752916e-05,25.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0699088145896656 0.00602118695926479 367.99999999999966
3 128 10528 0.0274506079027355 0.002316846624060842 288.9999999999993
4 256 10528 0.0254559270516717 0.0021464875636995062 267.99999999999966
5 512 10528 0.0227963525835866 0.0019198386716094973 239.99999999999974
6 1024 10528 0.0211816109422492 0.0017825061322593871 222.99999999999957
7 2048 10528 0.0196618541033434 0.0016534415568043581 206.99999999999932
8 4096 100000 0.00025 2.083572086752916e-05 25.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.636968085106383,0.0809720197955972,3353.0000000000005
128,5264,0.6396276595744681,0.08153497972875767,3367.0
256,5264,0.6137917933130699,0.0762202069454938,3231.0
512,5264,0.6177811550151976,0.07701918343526826,3252.0
1024,5264,0.518806990881459,0.059136525293307485,2731.0
2048,5264,0.3541033434650456,0.035770858082894375,1864.0
4096,5264,0.3180091185410334,0.031391637988989696,1674.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.636968085106383 0.0809720197955972 3353.0000000000005
3 128 5264 0.6396276595744681 0.08153497972875767 3367.0
4 256 5264 0.6137917933130699 0.0762202069454938 3231.0
5 512 5264 0.6177811550151976 0.07701918343526826 3252.0
6 1024 5264 0.518806990881459 0.059136525293307485 2731.0
7 2048 5264 0.3541033434650456 0.035770858082894375 1864.0
8 4096 5264 0.3180091185410334 0.031391637988989696 1674.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.1175911854103343,0.010370833329443885,618.9999999999998
128,5264,0.0818768996960486,0.007093372523371166,430.99999999999983
256,5264,0.0797872340425532,0.006905245826546502,420.0
512,5264,0.0761778115501519,0.0065812201662578396,400.99999999999955
1024,5264,0.0649696048632218,0.005582380453688085,341.99999999999955
2048,5264,0.0609802431610942,0.005229508237115432,320.99999999999983
4096,100000,0.00114,9.504967368278994e-05,114.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.1175911854103343 0.010370833329443885 618.9999999999998
3 128 5264 0.0818768996960486 0.007093372523371166 430.99999999999983
4 256 5264 0.0797872340425532 0.006905245826546502 420.0
5 512 5264 0.0761778115501519 0.0065812201662578396 400.99999999999955
6 1024 5264 0.0649696048632218 0.005582380453688085 341.99999999999955
7 2048 5264 0.0609802431610942 0.005229508237115432 320.99999999999983
8 4096 100000 0.00114 9.504967368278994e-05 114.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0695288753799392,0.005987356867991012,365.99999999999994
128,10528,0.0343844984802431,0.0029115494911087225,361.9999999999994
256,10528,0.0303001519756838,0.0025607731452100824,318.99999999999903
512,10528,0.0293503039513677,0.002479391381873808,308.99999999999915
1024,10528,0.0274506079027355,0.002316846624060842,288.9999999999993
2048,10528,0.0259308510638297,0.0021870202335232403,272.9999999999991
4096,100000,0.00028,2.3336328313527943e-05,27.999999999999996
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0695288753799392 0.005987356867991012 365.99999999999994
3 128 10528 0.0343844984802431 0.0029115494911087225 361.9999999999994
4 256 10528 0.0303001519756838 0.0025607731452100824 318.99999999999903
5 512 10528 0.0293503039513677 0.002479391381873808 308.99999999999915
6 1024 10528 0.0274506079027355 0.002316846624060842 288.9999999999993
7 2048 10528 0.0259308510638297 0.0021870202335232403 272.9999999999991
8 4096 100000 0.00028 2.3336328313527943e-05 27.999999999999996

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,10528,0.0365691489361702,0.0030997327267009434,384.9999999999999
128,10528,0.0250759878419452,0.00211407446256473,263.99999999999903
256,10528,0.0237462006079027,0.0020007197211502348,249.9999999999996
512,10528,0.0246960486322188,0.002081672938477608,259.99999999999955
1024,10528,0.021466565349544,0.0018067261883287777,225.99999999999923
2048,52633,0.0041798871430471,0.00034899302865942783,219.99999999999804
4096,100000,0.00039,3.250581082292481e-05,39.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 10528 0.0365691489361702 0.0030997327267009434 384.9999999999999
3 128 10528 0.0250759878419452 0.00211407446256473 263.99999999999903
4 256 10528 0.0237462006079027 0.0020007197211502348 249.9999999999996
5 512 10528 0.0246960486322188 0.002081672938477608 259.99999999999955
6 1024 10528 0.021466565349544 0.0018067261883287777 225.99999999999923
7 2048 52633 0.0041798871430471 0.00034899302865942783 219.99999999999804
8 4096 100000 0.00039 3.250581082292481e-05 39.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,10528,0.0273556231003039,0.0023087270282637906,287.99999999999943
128,15792,0.0165273556231003,0.0013878240888163251,260.99999999999994
256,15792,0.0151975683890577,0.001275372268384034,239.9999999999992
512,15792,0.0139311043566362,0.0011684046283987337,219.99999999999886
1024,15792,0.0136778115501519,0.0011470262137326381,215.9999999999988
2048,21055,0.0107812871052006,0.0009029109897770171,226.99999999999864
4096,100000,0.00012,1.0000550042188472e-05,12.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 10528 0.0273556231003039 0.0023087270282637906 287.99999999999943
3 128 15792 0.0165273556231003 0.0013878240888163251 260.99999999999994
4 256 15792 0.0151975683890577 0.001275372268384034 239.9999999999992
5 512 15792 0.0139311043566362 0.0011684046283987337 219.99999999999886
6 1024 15792 0.0136778115501519 0.0011470262137326381 215.9999999999988
7 2048 21055 0.0107812871052006 0.0009029109897770171 226.99999999999864
8 4096 100000 0.00012 1.0000550042188472e-05 12.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,10528,0.0254559270516717,0.0021464875636995062,267.99999999999966
128,15792,0.014564336372847,0.0012218726962905935,229.99999999999983
256,15792,0.0138044579533941,0.0011577147919119568,217.99999999999963
512,21055,0.0108762764189028,0.0009109061650477424,228.99999999999847
1024,21055,0.0116361909285205,0.0009748929168520437,244.99999999999912
2048,21055,0.0105913084777962,0.000886922750279795,222.999999999999
4096,100000,0.00011,9.167128851905737e-06,11.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 10528 0.0254559270516717 0.0021464875636995062 267.99999999999966
3 128 15792 0.014564336372847 0.0012218726962905935 229.99999999999983
4 256 15792 0.0138044579533941 0.0011577147919119568 217.99999999999963
5 512 21055 0.0108762764189028 0.0009109061650477424 228.99999999999847
6 1024 21055 0.0116361909285205 0.0009748929168520437 244.99999999999912
7 2048 21055 0.0105913084777962 0.000886922750279795 222.999999999999
8 4096 100000 0.00011 9.167128851905737e-06 11.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.1244300911854103,0.011012272752173091,654.9999999999999
128,5264,0.0978343465045592,0.008543059264558006,514.9999999999997
256,5264,0.1016337386018237,0.008891685588207632,535.0
512,5264,0.0985942249240121,0.008612676720513468,518.9999999999997
1024,5264,0.0875759878419452,0.007608447385851647,460.99999999999955
2048,10528,0.0227963525835866,0.0019198386716094973,239.99999999999974
4096,100000,0.00204,0.00017015915747353727,204.00000000000003
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.1244300911854103 0.011012272752173091 654.9999999999999
3 128 5264 0.0978343465045592 0.008543059264558006 514.9999999999997
4 256 5264 0.1016337386018237 0.008891685588207632 535.0
5 512 5264 0.0985942249240121 0.008612676720513468 518.9999999999997
6 1024 5264 0.0875759878419452 0.007608447385851647 460.99999999999955
7 2048 10528 0.0227963525835866 0.0019198386716094973 239.99999999999974
8 4096 100000 0.00204 0.00017015915747353727 204.00000000000003

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0438829787234042,0.0037325969956982785,230.99999999999972
128,10528,0.0256458966565349,0.0021626984583631437,269.99999999999943
256,10528,0.0222264437689969,0.0018713446265007372,233.99999999999935
512,10528,0.0203267477203647,0.0017098847247569715,213.99999999999957
1024,10528,0.020991641337386,0.0017663630187246815,220.9999999999998
2048,15792,0.0169072948328267,0.0014199787806923325,266.99999999999926
4096,100000,0.00024,2.0002200337376763e-05,24.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0438829787234042 0.0037325969956982785 230.99999999999972
3 128 10528 0.0256458966565349 0.0021626984583631437 269.99999999999943
4 256 10528 0.0222264437689969 0.0018713446265007372 233.99999999999935
5 512 10528 0.0203267477203647 0.0017098847247569715 213.99999999999957
6 1024 10528 0.020991641337386 0.0017663630187246815 220.9999999999998
7 2048 15792 0.0169072948328267 0.0014199787806923325 266.99999999999926
8 4096 100000 0.00024 2.0002200337376763e-05 24.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,10528,0.038563829787234,0.003271894132729214,405.99999999999955
128,10528,0.0225113981762917,0.0018955884093644348,236.999999999999
256,15792,0.0193135764944275,0.0016238900910929832,304.9999999999991
512,15792,0.0177304964539007,0.0014896863766280521,279.99999999999983
1024,15792,0.017160587639311,0.0014414215710746303,270.9999999999993
2048,15792,0.0162107396149949,0.0013610372094395862,255.99999999999946
4096,100000,0.00015,1.250085945736501e-05,14.999999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 10528 0.038563829787234 0.003271894132729214 405.99999999999955
3 128 10528 0.0225113981762917 0.0018955884093644348 236.999999999999
4 256 15792 0.0193135764944275 0.0016238900910929832 304.9999999999991
5 512 15792 0.0177304964539007 0.0014896863766280521 279.99999999999983
6 1024 15792 0.017160587639311 0.0014414215710746303 270.9999999999993
7 2048 15792 0.0162107396149949 0.0013610372094395862 255.99999999999946
8 4096 100000 0.00015 1.250085945736501e-05 14.999999999999998

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.636968085106383,0.0809720197955972,3353.0000000000005
128,5264,0.6396276595744681,0.08153497972875767,3367.0
256,5264,0.6137917933130699,0.0762202069454938,3231.0
512,5264,0.6177811550151976,0.07701918343526826,3252.0
1024,5264,0.518806990881459,0.059136525293307485,2731.0
2048,5264,0.3541033434650456,0.035770858082894375,1864.0
4096,5264,0.3180091185410334,0.031391637988989696,1674.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.636968085106383 0.0809720197955972 3353.0000000000005
3 128 5264 0.6396276595744681 0.08153497972875767 3367.0
4 256 5264 0.6137917933130699 0.0762202069454938 3231.0
5 512 5264 0.6177811550151976 0.07701918343526826 3252.0
6 1024 5264 0.518806990881459 0.059136525293307485 2731.0
7 2048 5264 0.3541033434650456 0.035770858082894375 1864.0
8 4096 5264 0.3180091185410334 0.031391637988989696 1674.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.1073328267477203,0.009417167606857957,564.9999999999997
128,5264,0.0780775075987842,0.006751615035668124,411.0
256,5264,0.0775075987841945,0.0067004628061660965,407.99999999999983
512,5264,0.0752279635258358,0.006496143143926547,395.99999999999966
1024,5264,0.0678191489361702,0.005835277985815512,356.99999999999994
2048,5264,0.0600303951367781,0.005145693617492952,315.99999999999994
4096,100000,0.00116,9.671809923239572e-05,116.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.1073328267477203 0.009417167606857957 564.9999999999997
3 128 5264 0.0780775075987842 0.006751615035668124 411.0
4 256 5264 0.0775075987841945 0.0067004628061660965 407.99999999999983
5 512 5264 0.0752279635258358 0.006496143143926547 395.99999999999966
6 1024 5264 0.0678191489361702 0.005835277985815512 356.99999999999994
7 2048 5264 0.0600303951367781 0.005145693617492952 315.99999999999994
8 4096 100000 0.00116 9.671809923239572e-05 116.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.0543313069908814,0.004644430295403956,285.9999999999997
128,10528,0.0287803951367781,0.002430597357511055,302.99999999999983
256,10528,0.0268806990881458,0.0022681399488484466,282.999999999999
512,10528,0.0249810030395136,0.002105972996532479,262.99999999999915
1024,10528,0.0230813069908814,0.001944095416857694,242.99999999999937
2048,10528,0.0235562310030395,0.0019845377421251476,247.99999999999986
4096,100000,0.00029,2.4169879414670525e-05,29.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.0543313069908814 0.004644430295403956 285.9999999999997
3 128 10528 0.0287803951367781 0.002430597357511055 302.99999999999983
4 256 10528 0.0268806990881458 0.0022681399488484466 282.999999999999
5 512 10528 0.0249810030395136 0.002105972996532479 262.99999999999915
6 1024 10528 0.0230813069908814 0.001944095416857694 242.99999999999937
7 2048 10528 0.0235562310030395 0.0019845377421251476 247.99999999999986
8 4096 100000 0.00029 2.4169879414670525e-05 29.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.5083586626139818,0.05745079247636664,2676.0
128,5264,0.3879179331306991,0.04008199450020078,2042.0000000000002
256,5264,0.364741641337386,0.03710440702030626,1920.0
512,5264,0.3478343465045592,0.03499441487180499,1830.9999999999995
1024,5264,0.3345364741641337,0.033369810593094074,1760.9999999999998
2048,5264,0.1073328267477203,0.009417167606857957,564.9999999999997
4096,5264,0.0381838905775075,0.00323907625680131,200.99999999999946
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.5083586626139818 0.05745079247636664 2676.0
3 128 5264 0.3879179331306991 0.04008199450020078 2042.0000000000002
4 256 5264 0.364741641337386 0.03710440702030626 1920.0
5 512 5264 0.3478343465045592 0.03499441487180499 1830.9999999999995
6 1024 5264 0.3345364741641337 0.033369810593094074 1760.9999999999998
7 2048 5264 0.1073328267477203 0.009417167606857957 564.9999999999997
8 4096 5264 0.0381838905775075 0.00323907625680131 200.99999999999946

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.4316109422492401,0.04598806248392684,2272.0
128,5264,0.2809650455927052,0.0271127759679356,1479.0
256,5264,0.2515197568389057,0.02385343933427242,1323.9999999999995
512,5264,0.2386018237082066,0.022460490669208744,1255.9999999999995
1024,5264,0.2313829787234042,0.021691485903849506,1217.9999999999995
2048,5264,0.1988981762917933,0.018310882399525497,1047.0
4096,10528,0.0241261398176291,0.0020330923404081602,253.99999999999918
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.4316109422492401 0.04598806248392684 2272.0
3 128 5264 0.2809650455927052 0.0271127759679356 1479.0
4 256 5264 0.2515197568389057 0.02385343933427242 1323.9999999999995
5 512 5264 0.2386018237082066 0.022460490669208744 1255.9999999999995
6 1024 5264 0.2313829787234042 0.021691485903849506 1217.9999999999995
7 2048 5264 0.1988981762917933 0.018310882399525497 1047.0
8 4096 10528 0.0241261398176291 0.0020330923404081602 253.99999999999918

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3903875379939209,0.040405346212274096,2054.9999999999995
128,5264,0.2405015197568389,0.02266397103267348,1266.0
256,5264,0.2159954407294833,0.020074146889335953,1137.0
512,5264,0.2002279635258358,0.018446781884206676,1053.9999999999995
1024,5264,0.1973784194528875,0.018155821680927553,1038.9999999999998
2048,5264,0.1903495440729483,0.01744214849069614,1001.9999999999998
4096,10528,0.020991641337386,0.0017663630187246815,220.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3903875379939209 0.040405346212274096 2054.9999999999995
3 128 5264 0.2405015197568389 0.02266397103267348 1266.0
4 256 5264 0.2159954407294833 0.020074146889335953 1137.0
5 512 5264 0.2002279635258358 0.018446781884206676 1053.9999999999995
6 1024 5264 0.1973784194528875 0.018155821680927553 1038.9999999999998
7 2048 5264 0.1903495440729483 0.01744214849069614 1001.9999999999998
8 4096 10528 0.020991641337386 0.0017663630187246815 220.9999999999998

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.6259498480243161,0.07867931965075214,3295.0
128,5264,0.5583206686930091,0.06583070229097776,2939.0
256,5264,0.5526215805471124,0.06483211115106036,2908.9999999999995
512,5264,0.5391337386018237,0.06251446315798959,2838.0
1024,5264,0.5098784194528876,0.05769393705396664,2684.0000000000005
2048,5264,0.2674772036474164,0.025604890903806354,1408.0
4096,5264,0.0921352583586626,0.008022635037369774,484.9999999999999
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.6259498480243161 0.07867931965075214 3295.0
3 128 5264 0.5583206686930091 0.06583070229097776 2939.0
4 256 5264 0.5526215805471124 0.06483211115106036 2908.9999999999995
5 512 5264 0.5391337386018237 0.06251446315798959 2838.0
6 1024 5264 0.5098784194528876 0.05769393705396664 2684.0000000000005
7 2048 5264 0.2674772036474164 0.025604890903806354 1408.0
8 4096 5264 0.0921352583586626 0.008022635037369774 484.9999999999999

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.4373100303951368,0.046788885074059094,2302.0
128,5264,0.315919452887538,0.031144661244724814,1663.0
256,5264,0.2980623100303951,0.029061892102767328,1568.9999999999998
512,5264,0.2870440729483283,0.027800879955706126,1511.0
1024,5264,0.2840045592705167,0.027456158538678666,1495.0
2048,5264,0.2562689969604863,0.024371098470341246,1348.9999999999998
4096,10528,0.0359992401215805,0.00305060376648969,378.9999999999995
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.4373100303951368 0.046788885074059094 2302.0
3 128 5264 0.315919452887538 0.031144661244724814 1663.0
4 256 5264 0.2980623100303951 0.029061892102767328 1568.9999999999998
5 512 5264 0.2870440729483283 0.027800879955706126 1511.0
6 1024 5264 0.2840045592705167 0.027456158538678666 1495.0
7 2048 5264 0.2562689969604863 0.024371098470341246 1348.9999999999998
8 4096 10528 0.0359992401215805 0.00305060376648969 378.9999999999995

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3936170212765957,0.0408300073999609,2072.0
128,5264,0.2572188449848024,0.024474993793634048,1353.9999999999998
256,5264,0.236322188449848,0.022216927636340866,1244.0
512,5264,0.2203647416413373,0.020530411940441584,1159.9999999999995
1024,5264,0.2203647416413373,0.020530411940441584,1159.9999999999995
2048,5264,0.2078267477203647,0.01922734698807005,1093.9999999999998
4096,10528,0.0237462006079027,0.0020007197211502348,249.9999999999996
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3936170212765957 0.0408300073999609 2072.0
3 128 5264 0.2572188449848024 0.024474993793634048 1353.9999999999998
4 256 5264 0.236322188449848 0.022216927636340866 1244.0
5 512 5264 0.2203647416413373 0.020530411940441584 1159.9999999999995
6 1024 5264 0.2203647416413373 0.020530411940441584 1159.9999999999995
7 2048 5264 0.2078267477203647 0.01922734698807005 1093.9999999999998
8 4096 10528 0.0237462006079027 0.0020007197211502348 249.9999999999996

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.9587765957446808,0.23335321086836192,5047.0
128,5264,0.944338905775076,0.21392742431016676,4971.0
256,5264,0.9456686930091184,0.2155098179872279,4977.999999999999
512,5264,0.9439589665653496,0.2134816768834944,4969.0
1024,5264,0.9283814589665652,0.19724049628061424,4886.999999999999
2048,5264,0.8763297872340425,0.15985272004411766,4613.0
4096,5264,0.8284574468085106,0.13662860463433912,4361.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.9587765957446808 0.23335321086836192 5047.0
3 128 5264 0.944338905775076 0.21392742431016676 4971.0
4 256 5264 0.9456686930091184 0.2155098179872279 4977.999999999999
5 512 5264 0.9439589665653496 0.2134816768834944 4969.0
6 1024 5264 0.9283814589665652 0.19724049628061424 4886.999999999999
7 2048 5264 0.8763297872340425 0.15985272004411766 4613.0
8 4096 5264 0.8284574468085106 0.13662860463433912 4361.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.559080547112462,0.06596473909805234,2943.0
128,5264,0.4678951367781155,0.05121800243006214,2463.0
256,5264,0.4532674772036474,0.049071400643614704,2386.0
512,5264,0.4502279635258359,0.048631968564752825,2370.0
1024,5264,0.4262917933130699,0.045247241414411166,2244.0
2048,5264,0.4046352583586626,0.042294622550035466,2130.0
4096,5264,0.0773176291793313,0.0066834185002044855,406.99999999999994
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.559080547112462 0.06596473909805234 2943.0
3 128 5264 0.4678951367781155 0.05121800243006214 2463.0
4 256 5264 0.4532674772036474 0.049071400643614704 2386.0
5 512 5264 0.4502279635258359 0.048631968564752825 2370.0
6 1024 5264 0.4262917933130699 0.045247241414411166 2244.0
7 2048 5264 0.4046352583586626 0.042294622550035466 2130.0
8 4096 5264 0.0773176291793313 0.0066834185002044855 406.99999999999994

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.4071048632218845,0.04262630471820339,2143.0
128,5264,0.281724924012158,0.02719849668814711,1482.9999999999995
256,5264,0.2604483282674772,0.024829151186504417,1371.0
512,5264,0.2496200607902735,0.023647218339284293,1313.9999999999995
1024,5264,0.25,0.023688424222606863,1316.0
2048,5264,0.2420212765957446,0.022827091512257702,1273.9999999999995
4096,10528,0.0310600303951367,0.0026259311852924183,326.9999999999992
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.4071048632218845 0.04262630471820339 2143.0
3 128 5264 0.281724924012158 0.02719849668814711 1482.9999999999995
4 256 5264 0.2604483282674772 0.024829151186504417 1371.0
5 512 5264 0.2496200607902735 0.023647218339284293 1313.9999999999995
6 1024 5264 0.25 0.023688424222606863 1316.0
7 2048 5264 0.2420212765957446 0.022827091512257702 1273.9999999999995
8 4096 10528 0.0310600303951367 0.0026259311852924183 326.9999999999992

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3854483282674772,0.03975983651029025,2029.0
128,5264,0.3185790273556231,0.031459115673202365,1677.0
256,5264,0.309080547112462,0.03034118098825833,1627.0
512,5264,0.3020516717325228,0.02952294321846627,1590.0
1024,5264,0.293693009118541,0.028559679253521097,1546.0
2048,5264,0.0946048632218845,0.008247783460578861,498.0
4096,10528,0.0358092705167173,0.0030342333628010643,376.9999999999997
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3854483282674772 0.03975983651029025 2029.0
3 128 5264 0.3185790273556231 0.031459115673202365 1677.0
4 256 5264 0.309080547112462 0.03034118098825833 1627.0
5 512 5264 0.3020516717325228 0.02952294321846627 1590.0
6 1024 5264 0.293693009118541 0.028559679253521097 1546.0
7 2048 5264 0.0946048632218845 0.008247783460578861 498.0
8 4096 10528 0.0358092705167173 0.0030342333628010643 376.9999999999997

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.2813449848024316,0.02715562594245713,1480.9999999999998
128,5264,0.2167553191489361,0.020153329623719185,1140.9999999999995
256,5264,0.2053571428571428,0.018972913280972947,1080.9999999999998
512,5264,0.1962386018237082,0.018039702664670587,1033.0
1024,5264,0.1933890577507598,0.017750064191601855,1017.9999999999995
2048,5264,0.1685030395136778,0.01525967596159794,886.9999999999999
4096,10528,0.0215615501519756,0.001814800977090858,226.99999999999912
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.2813449848024316 0.02715562594245713 1480.9999999999998
3 128 5264 0.2167553191489361 0.020153329623719185 1140.9999999999995
4 256 5264 0.2053571428571428 0.018972913280972947 1080.9999999999998
5 512 5264 0.1962386018237082 0.018039702664670587 1033.0
6 1024 5264 0.1933890577507598 0.017750064191601855 1017.9999999999995
7 2048 5264 0.1685030395136778 0.01525967596159794 886.9999999999999
8 4096 10528 0.0215615501519756 0.001814800977090858 226.99999999999912

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.243161094224924,0.022949628736110284,1279.9999999999998
128,5264,0.1856003039513677,0.016963145032539706,976.9999999999995
256,5264,0.173822188449848,0.01578617630577761,914.9999999999999
512,5264,0.166983282674772,0.01510981435822023,878.9999999999998
1024,5264,0.1593844984802431,0.014364245161496703,838.9999999999998
2048,5264,0.1521656534954407,0.01366165487854265,800.9999999999999
4096,15792,0.014564336372847,0.0012218726962905935,229.99999999999983
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.243161094224924 0.022949628736110284 1279.9999999999998
3 128 5264 0.1856003039513677 0.016963145032539706 976.9999999999995
4 256 5264 0.173822188449848 0.01578617630577761 914.9999999999999
5 512 5264 0.166983282674772 0.01510981435822023 878.9999999999998
6 1024 5264 0.1593844984802431 0.014364245161496703 838.9999999999998
7 2048 5264 0.1521656534954407 0.01366165487854265 800.9999999999999
8 4096 15792 0.014564336372847 0.0012218726962905935 229.99999999999983

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.5953647416413373,0.0726251577026028,3134.0
128,5264,0.5429331306990881,0.063160963986573,2858.0
256,5264,0.5300151975683891,0.06098257534478824,2790.0000000000005
512,5264,0.5307750759878419,0.06110918730839732,2794.0
1024,5264,0.5049392097264438,0.056906226493265155,2658.0
2048,5264,0.2672872340425531,0.025583835420806667,1406.9999999999995
4096,5264,0.0970744680851063,0.008473495538973252,510.99999999999955
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.5953647416413373 0.0726251577026028 3134.0
3 128 5264 0.5429331306990881 0.063160963986573 2858.0
4 256 5264 0.5300151975683891 0.06098257534478824 2790.0000000000005
5 512 5264 0.5307750759878419 0.06110918730839732 2794.0
6 1024 5264 0.5049392097264438 0.056906226493265155 2658.0
7 2048 5264 0.2672872340425531 0.025583835420806667 1406.9999999999995
8 4096 5264 0.0970744680851063 0.008473495538973252 510.99999999999955

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3768996960486322,0.03865376393916575,1984.0
128,5264,0.2912234042553192,0.028277078147982637,1533.0000000000002
256,5264,0.2769756838905775,0.02666410002335362,1457.9999999999998
512,5264,0.2689969604863222,0.025773515193305396,1416.0000000000002
1024,5264,0.2631079027355623,0.02512187629950391,1384.9999999999998
2048,5264,0.2479103343465045,0.02346202821500787,1304.9999999999998
4096,10528,0.0349544072948328,0.002960603114667504,367.99999999999966
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3768996960486322 0.03865376393916575 1984.0
3 128 5264 0.2912234042553192 0.028277078147982637 1533.0000000000002
4 256 5264 0.2769756838905775 0.02666410002335362 1457.9999999999998
5 512 5264 0.2689969604863222 0.025773515193305396 1416.0000000000002
6 1024 5264 0.2631079027355623 0.02512187629950391 1384.9999999999998
7 2048 5264 0.2479103343465045 0.02346202821500787 1304.9999999999998
8 4096 10528 0.0349544072948328 0.002960603114667504 367.99999999999966

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3181990881458966,0.0314141248052785,1674.9999999999995
128,5264,0.2239741641337386,0.020909097938161536,1179.0
256,5264,0.2085866261398176,0.01930578057020138,1097.9999999999998
512,5264,0.2066869300911854,0.019109825830462723,1088.0
1024,5264,0.1954787234042553,0.017962373817176802,1028.9999999999998
2048,5264,0.1895896656534954,0.017365335144093663,997.9999999999998
4096,10528,0.0226063829787234,0.0019036711100027803,237.99999999999994
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3181990881458966 0.0314141248052785 1674.9999999999995
3 128 5264 0.2239741641337386 0.020909097938161536 1179.0
4 256 5264 0.2085866261398176 0.01930578057020138 1097.9999999999998
5 512 5264 0.2066869300911854 0.019109825830462723 1088.0
6 1024 5264 0.1954787234042553 0.017962373817176802 1028.9999999999998
7 2048 5264 0.1895896656534954 0.017365335144093663 997.9999999999998
8 4096 10528 0.0226063829787234 0.0019036711100027803 237.99999999999994

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.9587765957446808,0.23335321086836192,5047.0
128,5264,0.944338905775076,0.21392742431016676,4971.0
256,5264,0.9456686930091184,0.2155098179872279,4977.999999999999
512,5264,0.9439589665653496,0.2134816768834944,4969.0
1024,5264,0.9283814589665652,0.19724049628061424,4886.999999999999
2048,5264,0.8763297872340425,0.15985272004411766,4613.0
4096,5264,0.8284574468085106,0.13662860463433912,4361.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.9587765957446808 0.23335321086836192 5047.0
3 128 5264 0.944338905775076 0.21392742431016676 4971.0
4 256 5264 0.9456686930091184 0.2155098179872279 4977.999999999999
5 512 5264 0.9439589665653496 0.2134816768834944 4969.0
6 1024 5264 0.9283814589665652 0.19724049628061424 4886.999999999999
7 2048 5264 0.8763297872340425 0.15985272004411766 4613.0
8 4096 5264 0.8284574468085106 0.13662860463433912 4361.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.5298252279635258,0.060950951675050935,2789.0
128,5264,0.4532674772036474,0.049071400643614704,2386.0
256,5264,0.4559270516717325,0.04945774460551566,2399.9999999999995
512,5264,0.4414893617021276,0.047380894247510574,2323.9999999999995
1024,5264,0.4213525835866261,0.044564952358048915,2217.9999999999995
2048,5264,0.4019756838905775,0.041938833845470036,2116.0
4096,5264,0.0778875379939209,0.006734561072627154,409.9999999999996
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.5298252279635258 0.060950951675050935 2789.0
3 128 5264 0.4532674772036474 0.049071400643614704 2386.0
4 256 5264 0.4559270516717325 0.04945774460551566 2399.9999999999995
5 512 5264 0.4414893617021276 0.047380894247510574 2323.9999999999995
6 1024 5264 0.4213525835866261 0.044564952358048915 2217.9999999999995
7 2048 5264 0.4019756838905775 0.041938833845470036 2116.0
8 4096 5264 0.0778875379939209 0.006734561072627154 409.9999999999996

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.378419452887538,0.03884937828659363,1992.0
128,5264,0.2674772036474164,0.025604890903806354,1408.0
256,5264,0.2530395136778115,0.024018761866976934,1331.9999999999998
512,5264,0.2420212765957446,0.022827091512257702,1273.9999999999995
1024,5264,0.2348024316109422,0.022054922269394428,1235.9999999999998
2048,5264,0.2325227963525835,0.021812466388903662,1223.9999999999995
4096,10528,0.0293503039513677,0.002479391381873808,308.99999999999915
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.378419452887538 0.03884937828659363 1992.0
3 128 5264 0.2674772036474164 0.025604890903806354 1408.0
4 256 5264 0.2530395136778115 0.024018761866976934 1331.9999999999998
5 512 5264 0.2420212765957446 0.022827091512257702 1273.9999999999995
6 1024 5264 0.2348024316109422 0.022054922269394428 1235.9999999999998
7 2048 5264 0.2325227963525835 0.021812466388903662 1223.9999999999995
8 4096 10528 0.0293503039513677 0.002479391381873808 308.99999999999915

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.902355623100304,0.176233860490154,4750.000000000001
128,5264,0.8717705167173252,0.15731423502706487,4589.0
256,5264,0.854483282674772,0.148386101186325,4498.0
512,5264,0.8514437689969605,0.14691774938906965,4482.0
1024,5264,0.8474544072948328,0.14503177965348668,4461.0
2048,5264,0.5364741641337386,0.06206481317040313,2824.0
4096,5264,0.3729103343465045,0.0381423495423282,1962.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.902355623100304 0.176233860490154 4750.000000000001
3 128 5264 0.8717705167173252 0.15731423502706487 4589.0
4 256 5264 0.854483282674772 0.148386101186325 4498.0
5 512 5264 0.8514437689969605 0.14691774938906965 4482.0
6 1024 5264 0.8474544072948328 0.14503177965348668 4461.0
7 2048 5264 0.5364741641337386 0.06206481317040313 2824.0
8 4096 5264 0.3729103343465045 0.0381423495423282 1962.9999999999998

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8571428571428571,0.14969415714139434,4512.0
128,5264,0.7931231003039514,0.12304764251672351,4175.0
256,5264,0.7784954407294833,0.11804067619572267,4098.0
512,5264,0.776595744680851,0.1174128092857799,4087.9999999999995
1024,5264,0.7646276595744681,0.11356624491338829,4025.0
2048,5264,0.7025075987841946,0.09609458157939987,3698.0
4096,5264,0.2881838905775076,0.027930497891309525,1517.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8571428571428571 0.14969415714139434 4512.0
3 128 5264 0.7931231003039514 0.12304764251672351 4175.0
4 256 5264 0.7784954407294833 0.11804067619572267 4098.0
5 512 5264 0.776595744680851 0.1174128092857799 4087.9999999999995
6 1024 5264 0.7646276595744681 0.11356624491338829 4025.0
7 2048 5264 0.7025075987841946 0.09609458157939987 3698.0
8 4096 5264 0.2881838905775076 0.027930497891309525 1517.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8294072948328267,0.1370280002940526,4366.0
128,5264,0.7427811550151976,0.10698538759235554,3910.0
256,5264,0.726823708206687,0.102494916492331,3826.0
512,5264,0.7099164133738601,0.097992262366698,3736.9999999999995
1024,5264,0.6992781155015197,0.09528091286920859,3681.0
2048,5264,0.6850303951367781,0.09178419820353789,3606.0
4096,5264,0.2549392097264438,0.02422584917241799,1342.0000000000002
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8294072948328267 0.1370280002940526 4366.0
3 128 5264 0.7427811550151976 0.10698538759235554 3910.0
4 256 5264 0.726823708206687 0.102494916492331 3826.0
5 512 5264 0.7099164133738601 0.097992262366698 3736.9999999999995
6 1024 5264 0.6992781155015197 0.09528091286920859 3681.0
7 2048 5264 0.6850303951367781 0.09178419820353789 3606.0
8 4096 5264 0.2549392097264438 0.02422584917241799 1342.0000000000002

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.952127659574468,0.2237402733118209,5011.999999999999
128,5264,0.9342705167173252,0.20296018192442522,4918.0
256,5264,0.9304711246200608,0.19921898726543907,4898.0
512,5264,0.9291413373860182,0.19795374999805782,4891.0
1024,5264,0.9171732522796352,0.1874547595745174,4828.0
2048,5264,0.7494300911854104,0.10893221475643655,3945.0
4096,5264,0.5315349544072948,0.06123598736468405,2798.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.952127659574468 0.2237402733118209 5011.999999999999
3 128 5264 0.9342705167173252 0.20296018192442522 4918.0
4 256 5264 0.9304711246200608 0.19921898726543907 4898.0
5 512 5264 0.9291413373860182 0.19795374999805782 4891.0
6 1024 5264 0.9171732522796352 0.1874547595745174 4828.0
7 2048 5264 0.7494300911854104 0.10893221475643655 3945.0
8 4096 5264 0.5315349544072948 0.06123598736468405 2798.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8704407294832827,0.15658942672873288,4582.0
128,5264,0.8208586626139818,0.13350448791349223,4321.0
256,5264,0.8151595744680851,0.13124014003562634,4291.0
512,5264,0.8085106382978723,0.12867791716151256,4256.0
1024,5264,0.8033814589665653,0.12675647830941417,4229.0
2048,5264,0.7699468085106383,0.11525315651497492,4053.0000000000005
4096,5264,0.3495440729483283,0.03520549127486017,1840.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8704407294832827 0.15658942672873288 4582.0
3 128 5264 0.8208586626139818 0.13350448791349223 4321.0
4 256 5264 0.8151595744680851 0.13124014003562634 4291.0
5 512 5264 0.8085106382978723 0.12867791716151256 4256.0
6 1024 5264 0.8033814589665653 0.12675647830941417 4229.0
7 2048 5264 0.7699468085106383 0.11525315651497492 4053.0000000000005
8 4096 5264 0.3495440729483283 0.03520549127486017 1840.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.837386018237082,0.1404658089079346,4408.0
128,5264,0.7678571428571429,0.11458621931056301,4042.0000000000005
256,5264,0.7526595744680851,0.1098949630603957,3962.0
512,5264,0.7437310030395137,0.10726066135818801,3915.0
1024,5264,0.7357522796352584,0.10497684549815112,3873.0
2048,5264,0.7273936170212766,0.10265109945716466,3829.0
4096,5264,0.3020516717325228,0.02952294321846627,1590.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.837386018237082 0.1404658089079346 4408.0
3 128 5264 0.7678571428571429 0.11458621931056301 4042.0000000000005
4 256 5264 0.7526595744680851 0.1098949630603957 3962.0
5 512 5264 0.7437310030395137 0.10726066135818801 3915.0
6 1024 5264 0.7357522796352584 0.10497684549815112 3873.0
7 2048 5264 0.7273936170212766 0.10265109945716466 3829.0
8 4096 5264 0.3020516717325228 0.02952294321846627 1590.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.998290273556231,0.4119550124897787,5255.0
128,5264,0.9979103343465046,0.4020387142549051,5253.0
256,5264,0.9979103343465046,0.4020387142549051,5253.0
512,5264,0.9973404255319148,0.38990001695558896,5250.0
1024,5264,0.9977203647416414,0.3976871706739864,5252.0
2048,5264,0.9943009118541032,0.3498945884411908,5233.999999999999
4096,5264,0.9834726443768996,0.2895771073878175,5177.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.998290273556231 0.4119550124897787 5255.0
3 128 5264 0.9979103343465046 0.4020387142549051 5253.0
4 256 5264 0.9979103343465046 0.4020387142549051 5253.0
5 512 5264 0.9973404255319148 0.38990001695558896 5250.0
6 1024 5264 0.9977203647416414 0.3976871706739864 5252.0
7 2048 5264 0.9943009118541032 0.3498945884411908 5233.999999999999
8 4096 5264 0.9834726443768996 0.2895771073878175 5177.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.9230623100303952,0.19243356150150437,4859.0
128,5264,0.8985562310030395,0.17360924873637662,4730.0
256,5264,0.8936170212765957,0.17032880513057425,4704.0
512,5264,0.8928571428571429,0.16983656266599023,4700.0
1024,5264,0.8780395136778115,0.1608268194603586,4622.0
2048,5264,0.8609422492401215,0.1516020703440718,4532.0
4096,5264,0.4665653495440729,0.0510206360489559,2456.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.9230623100303952 0.19243356150150437 4859.0
3 128 5264 0.8985562310030395 0.17360924873637662 4730.0
4 256 5264 0.8936170212765957 0.17032880513057425 4704.0
5 512 5264 0.8928571428571429 0.16983656266599023 4700.0
6 1024 5264 0.8780395136778115 0.1608268194603586 4622.0
7 2048 5264 0.8609422492401215 0.1516020703440718 4532.0
8 4096 5264 0.4665653495440729 0.0510206360489559 2456.0

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8529635258358662,0.14764844721526915,4490.0
128,5264,0.7891337386018237,0.12165070271088807,4154.0
256,5264,0.7794452887537994,0.11835646289465096,4103.0
512,5264,0.7663373860182371,0.11410462186621395,4034.0
1024,5264,0.7646276595744681,0.11356624491338829,4025.0
2048,5264,0.7528495440729484,0.10995195349356313,3963.0
4096,5264,0.3206686930091185,0.031706977249001955,1687.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8529635258358662 0.14764844721526915 4490.0
3 128 5264 0.7891337386018237 0.12165070271088807 4154.0
4 256 5264 0.7794452887537994 0.11835646289465096 4103.0
5 512 5264 0.7663373860182371 0.11410462186621395 4034.0
6 1024 5264 0.7646276595744681 0.11356624491338829 4025.0
7 2048 5264 0.7528495440729484 0.10995195349356313 3963.0
8 4096 5264 0.3206686930091185 0.031706977249001955 1687.9999999999998

View File

@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8634118541033434,0.15286800617587304,4545.0
128,5264,0.8299772036474165,0.13726861727420125,4369.0
256,5264,0.8273176291793313,0.1361519971102999,4355.0
512,5264,0.8216185410334347,0.13381137539624244,4325.0
1024,5264,0.8130699088145896,0.1304258889002432,4280.0
2048,5264,0.5233662613981763,0.05988265576801599,2755.0
4096,5264,0.3655015197568389,0.037200442098062725,1924.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8634118541033434 0.15286800617587304 4545.0
3 128 5264 0.8299772036474165 0.13726861727420125 4369.0
4 256 5264 0.8273176291793313 0.1361519971102999 4355.0
5 512 5264 0.8216185410334347 0.13381137539624244 4325.0
6 1024 5264 0.8130699088145896 0.1304258889002432 4280.0
7 2048 5264 0.5233662613981763 0.05988265576801599 2755.0
8 4096 5264 0.3655015197568389 0.037200442098062725 1924.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.7862841945288754,0.12066764462334245,4139.0
128,5264,0.7351823708206687,0.1048161446276401,3870.0
256,5264,0.727773556231003,0.10275538779989046,3831.0
512,5264,0.7239741641337386,0.10171845392204981,3811.0
1024,5264,0.7154255319148937,0.09943238015706579,3766.0000000000005
2048,5264,0.6633738601823708,0.0867374413683899,3491.9999999999995
4096,5264,0.2684270516717325,0.025710243458687887,1412.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.7862841945288754 0.12066764462334245 4139.0
3 128 5264 0.7351823708206687 0.1048161446276401 3870.0
4 256 5264 0.727773556231003 0.10275538779989046 3831.0
5 512 5264 0.7239741641337386 0.10171845392204981 3811.0
6 1024 5264 0.7154255319148937 0.09943238015706579 3766.0000000000005
7 2048 5264 0.6633738601823708 0.0867374413683899 3491.9999999999995
8 4096 5264 0.2684270516717325 0.025710243458687887 1412.9999999999998

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.7435410334346505,0.10720553186924431,3914.0000000000005
128,5264,0.6793313069908815,0.09042598078385389,3576.0000000000005
256,5264,0.6745820668693009,0.08931093150519598,3551.0
512,5264,0.6639437689969605,0.08686638775393252,3495.0
1024,5264,0.6521656534954408,0.08424133089346608,3433.0
2048,5264,0.6405775075987842,0.08173696014562926,3372.0
4096,5264,0.2268237082066869,0.021209202765257062,1193.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.7435410334346505 0.10720553186924431 3914.0000000000005
3 128 5264 0.6793313069908815 0.09042598078385389 3576.0000000000005
4 256 5264 0.6745820668693009 0.08931093150519598 3551.0
5 512 5264 0.6639437689969605 0.08686638775393252 3495.0
6 1024 5264 0.6521656534954408 0.08424133089346608 3433.0
7 2048 5264 0.6405775075987842 0.08173696014562926 3372.0
8 4096 5264 0.2268237082066869 0.021209202765257062 1193.9999999999998

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.9445288753799392,0.2141513449299972,4972.0
128,5264,0.9264817629179332,0.195487263952138,4877.0
256,5264,0.9253419452887538,0.19445515554338988,4871.0
512,5264,0.9251519756838906,0.1942845433915268,4870.0
1024,5264,0.9116641337386018,0.1830827073922685,4799.0
2048,5264,0.748290273556231,0.108595135390262,3939.0
4096,5264,0.5338145896656535,0.06161752195556314,2810.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.9445288753799392 0.2141513449299972 4972.0
3 128 5264 0.9264817629179332 0.195487263952138 4877.0
4 256 5264 0.9253419452887538 0.19445515554338988 4871.0
5 512 5264 0.9251519756838906 0.1942845433915268 4870.0
6 1024 5264 0.9116641337386018 0.1830827073922685 4799.0
7 2048 5264 0.748290273556231 0.108595135390262 3939.0
8 4096 5264 0.5338145896656535 0.06161752195556314 2810.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8575227963525835,0.14988284156208842,4514.0
128,5264,0.8113601823708206,0.12976586607009988,4271.0
256,5264,0.8052811550151976,0.12746270583120645,4239.0
512,5264,0.7927431610942249,0.12291354165246304,4173.0
1024,5264,0.7917933130699089,0.12257927282507974,4168.0
2048,5264,0.7648176291793313,0.11362588722140576,4026.0
4096,5264,0.3478343465045592,0.03499441487180499,1830.9999999999995
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8575227963525835 0.14988284156208842 4514.0
3 128 5264 0.8113601823708206 0.12976586607009988 4271.0
4 256 5264 0.8052811550151976 0.12746270583120645 4239.0
5 512 5264 0.7927431610942249 0.12291354165246304 4173.0
6 1024 5264 0.7917933130699089 0.12257927282507974 4168.0
7 2048 5264 0.7648176291793313 0.11362588722140576 4026.0
8 4096 5264 0.3478343465045592 0.03499441487180499 1830.9999999999995

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8062310030395137,0.12781819127443927,4244.0
128,5264,0.7416413373860182,0.1066562864103282,3904.0
256,5264,0.7289133738601824,0.10306905531226573,3837.0
512,5264,0.7196048632218845,0.10054203270882656,3788.0000000000005
1024,5264,0.7156155015197568,0.09948249382407981,3767.0
2048,5264,0.7082066869300911,0.09755042532721536,3728.0
4096,5264,0.2815349544072948,0.027177058717590352,1482.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8062310030395137 0.12781819127443927 4244.0
3 128 5264 0.7416413373860182 0.1066562864103282 3904.0
4 256 5264 0.7289133738601824 0.10306905531226573 3837.0
5 512 5264 0.7196048632218845 0.10054203270882656 3788.0000000000005
6 1024 5264 0.7156155015197568 0.09948249382407981 3767.0
7 2048 5264 0.7082066869300911 0.09755042532721536 3728.0
8 4096 5264 0.2815349544072948 0.027177058717590352 1482.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.998290273556231,0.4119550124897787,5255.0
128,5264,0.9979103343465046,0.4020387142549051,5253.0
256,5264,0.9979103343465046,0.4020387142549051,5253.0
512,5264,0.9973404255319148,0.38990001695558896,5250.0
1024,5264,0.9977203647416414,0.3976871706739864,5252.0
2048,5264,0.9943009118541032,0.3498945884411908,5233.999999999999
4096,5264,0.9834726443768996,0.2895771073878175,5177.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.998290273556231 0.4119550124897787 5255.0
3 128 5264 0.9979103343465046 0.4020387142549051 5253.0
4 256 5264 0.9979103343465046 0.4020387142549051 5253.0
5 512 5264 0.9973404255319148 0.38990001695558896 5250.0
6 1024 5264 0.9977203647416414 0.3976871706739864 5252.0
7 2048 5264 0.9943009118541032 0.3498945884411908 5233.999999999999
8 4096 5264 0.9834726443768996 0.2895771073878175 5177.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.9202127659574468,0.1899824083599183,4844.0
128,5264,0.8962765957446809,0.17207741452555048,4718.0
256,5264,0.8919072948328267,0.16922574021648806,4695.0
512,5264,0.8848784194528876,0.1648527373653934,4658.0
1024,5264,0.8761398176291794,0.1597452499521742,4612.0
2048,5264,0.8573328267477204,0.1497884417749198,4513.0
4096,5264,0.4591565349544073,0.04992921073125611,2417.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.9202127659574468 0.1899824083599183 4844.0
3 128 5264 0.8962765957446809 0.17207741452555048 4718.0
4 256 5264 0.8919072948328267 0.16922574021648806 4695.0
5 512 5264 0.8848784194528876 0.1648527373653934 4658.0
6 1024 5264 0.8761398176291794 0.1597452499521742 4612.0
7 2048 5264 0.8573328267477204 0.1497884417749198 4513.0
8 4096 5264 0.4591565349544073 0.04992921073125611 2417.0

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@@ -0,0 +1,8 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.8404255319148937,0.14181625587553504,4424.0
128,5264,0.7790653495440729,0.11822999889791286,4101.0
256,5264,0.7695668693009119,0.11513148326421896,4051.0
512,5264,0.7591185410334347,0.11185553330329556,3996.0
1024,5264,0.7515197568389058,0.10955386070579232,3956.0
2048,5264,0.7463905775075987,0.10803643339325764,3928.9999999999995
4096,5264,0.3174392097264438,0.03132421197402735,1671.0000000000002
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.8404255319148937 0.14181625587553504 4424.0
3 128 5264 0.7790653495440729 0.11822999889791286 4101.0
4 256 5264 0.7695668693009119 0.11513148326421896 4051.0
5 512 5264 0.7591185410334347 0.11185553330329556 3996.0
6 1024 5264 0.7515197568389058 0.10955386070579232 3956.0
7 2048 5264 0.7463905775075987 0.10803643339325764 3928.9999999999995
8 4096 5264 0.3174392097264438 0.03132421197402735 1671.0000000000002