Fix wrong sim results; Add bpgd with decimation info passing over max iter plots

This commit is contained in:
2026-04-27 16:05:54 +02:00
parent f899942029
commit 4aa4799969
36 changed files with 645 additions and 86 deletions

View File

@@ -18,6 +18,11 @@
long=normalized min-sum
}
\DeclareAcronym{bsc}{
short=BSC,
long=binary symetric channel
}
\DeclareAcronym{spa}{
short=SPA,
long=sum-product algorithm

View File

@@ -1049,3 +1049,12 @@
month = jul,
year = {2014},
}
@misc{gidney_new_2023,
title = {New circuits and an open source decoder for the color code},
doi = {10.48550/arXiv.2312.08813},
publisher = {arXiv},
author = {Gidney, Craig and Jones, Cody},
month = dec,
year = {2023},
}

View File

@@ -991,8 +991,6 @@ standard circuit-based depolarizing noise model, etc.)}
\caption{Comparison of step sizes for $W=5$.}
\end{subfigure}
% TODO: Replace with new sim. results
\content{Replace with new sim. results}
\caption{
Comparison of the decoding performance of cold and warm-start
decoding under the $\llbracket 144,12,12 \rrbracket$ \ac{bb}.
@@ -1006,7 +1004,165 @@ standard circuit-based depolarizing noise model, etc.)}
}
\end{figure}
\content{LER over max iterations for windowed BPGD with decimation info passing}
\begin{figure}[H]
\centering
\hspace*{-6mm}
\begin{subfigure}{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
width=8cm,
height=6cm,
ymode=log,
% xmode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-3, ymax=1e-1,
grid=both,
legend pos = south west,
xtick={32,512,1024,2048,4096},
% xtick={0.001,0.0015,...,0.004},
xticklabels =
{$32$,$512$,$1{,}024$,,$2{,}048$,,$3{,}072$,,$4{,}096$},
xtick={32, 512, 1024, 1536, 2048, 2560, 3072, 3584, 4096},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
x tick label style={rotate=45, anchor=north east,
inner sep=1mm},
scaled x ticks=false,
xlabel={Number of BP iterations},
ylabel={Per-round-LER},
% extra description/.code={
% \node[rotate=90, anchor=south]
% at ([xshift=10mm]current axis.east)
% {Warm s. (---), Cold s. (- - -)};
% },
]
\foreach \W/\col/\mark in
{3/KITred/triangle,4/KITblue/diamond,5/KITorange/square} {
\edef\temp{\noexpand
\addplot+[mark=\mark, densely dashed, forget plot, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_False/F_1/W_\W/LERs.csv};
}
\temp
}
\foreach \W/\col/\mark in
{3/KITred/triangle*,4/KITblue/diamond*,5/KITorange/square*} {
\edef\temp{\noexpand
\addplot+[mark=\mark, solid, mark
options={fill=\col}, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_True/F_1/W_\W/LERs.csv};
}
\temp
\addlegendentryexpanded{$W = \W$}
}
\end{axis}
\end{tikzpicture}
\caption{Comparison of window sizes for $F=1$.}
\end{subfigure}%
\hfill%
\begin{subfigure}{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
width=8cm,
height=6cm,
ymode=log,
% xmode=log,
legend style={
cells={anchor=west},
cells={align=left},
},
enlargelimits=false,
ymin=1e-3, ymax=1e-1,
grid=both,
legend pos = south west,
xtick={32,512,1024,2048,4096},
% xtick={0.001,0.0015,...,0.004},
xticklabels =
{$32$,$512$,$1{,}024$,,$2{,}048$,,$3{,}072$,,$4{,}096$},
xtick={32, 512, 1024, 1536, 2048, 2560, 3072, 3584, 4096},
xticklabel style={/pgf/number format/fixed},
xticklabel style={/pgf/number format/precision=4},
x tick label style={rotate=45, anchor=north east,
inner sep=1mm},
scaled x ticks=false,
xlabel={Number of BP iterations},
% ylabel={Per-round-LER},
yticklabels={\empty},
extra description/.code={
\node[rotate=90, anchor=south]
at ([xshift=10mm]current axis.east)
{Warm s. (---), Cold s. (- - -)};
},
]
\foreach \F/\col/\mark in
{3/KITred/triangle,2/KITblue/diamond,1/KITorange/square} {
\edef\temp{\noexpand
\addplot+[mark=\mark, densely dashed, forget plot, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_False/F_\F/W_5/LERs.csv};
}
\temp
}
\foreach \F/\col/\mark in
{3/KITred/triangle*,2/KITblue/diamond*,1/KITorange/square*} {
\edef\temp{\noexpand
\addplot+[mark=\mark, solid, mark
options={fill=\col}, \col]
table[
col sep=comma, x=max_iter,
y=LER_per_round,
]
{res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_True/F_\F/W_5/LERs.csv};
}
\temp
\addlegendentryexpanded{$F = \F$}
}
\end{axis}
\end{tikzpicture}
\caption{
Comparison of step sizes for $W=5$.
}
\end{subfigure}
\caption{
Comparison of the decoding performance of cold and warm-start
decoding for the $\llbracket 144,12,12 \rrbracket$ \ac{bb}
under circuit-level noise.
Decoding was performed using the \ac{bpgd} algorithm with
$T=1$.
The number of iterations refers to the outer \ac{bpgd}
iterations, i.e., the number of decimations.
The information used for the warm-start intialization
included only the messages on the Tanner graph.
$12$ rounds of syndrome extraction were performed and
standard circuit-based depolarizing noise was chosen as the noise model.
The physical error probabilty was fixed to $0.0025$.
}
\end{figure}
\begin{figure}[H]
\centering
@@ -1310,11 +1466,14 @@ standard circuit-based depolarizing noise model, etc.)}
decoding for the $\llbracket 144,12,12 \rrbracket$ \ac{bb}
under circuit-level noise.
Decoding was performed using the \ac{bpgd} algorithm with
$T=1$ and no limit on the number of outer iterations.
$T=1$.
The number of iterations refers to the outer \ac{bpgd}
iterations, i.e., the number of decimations.
The information used for the warm-start intialization
included only the messages on the Tanner graph.
$12$ rounds of syndrome extraction were performed and
standard circuit-based depolarizing noise was chosen as the noise model.
The physical error probabilty was fixed to $0.0025$.
}
\end{figure}

170
src/thesis/copy_sim_results.sh Executable file
View File

@@ -0,0 +1,170 @@
#!/bin/bash
BASE_PATH="/home/andreas/workspace/private/ma-sw-results/outputs/"
# Copy BP param exploration results
function post_process_LERs() {
local filename="$1"
python3 -c "
import pandas as pd
import numpy as np
df = pd.read_csv('${filename}')
df['LER_per_round'] = 1 - (1 - df['LER'])**(1/12)
df['num_errors'] = df['num_trials'] * df['LER']
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++%${#sp}:1}"
# done
# done
# done
# done
#
# # Copy BP over max iter. results
#
# echo -e "\rCopying BP over max. iter. results..."
# echo -n ' '
# for decoder in "WindowingSyndromeMinSumDecoder" "WindowingSyndromeSpaDecoder"; do
# for p in 0.001 0.0025 0.004; do
# for pass_soft_info in "True" "False"; do
# for F in 1 2 3; do
# for W in 3 4 5; do
# SRC_PATH="${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++%${#sp}:1}"
# done
# done
# done
# done
#
# # Copy BP over max iter. results
#
# echo -e "\rCopying one-shot simulation results..."
# echo -n ' '
# for decoder in "SyndromeMinSumDecoder" "SyndromeSpaDecoder" "SyndromeSpaGdDecoder"; do
# for max_iter in 32 200 5000; do
# SRC_PATH="${BASE_PATH}+rust_exp=whole_bp_bpgd,decoder.class_name=${decoder},decoder.max_iter=${max_iter},system.F=1,system.W=5/"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/one-shot/${decoder}/max_iter_${max_iter}/"
# mkdir -p ${DEST_DIR}
# DEST_FILE="${DEST_DIR}/LERs.csv"
# cp ${SRC_FILE} ${DEST_FILE}
# post_process_LERs ${DEST_FILE}
# printf "\b${sp:i++%${#sp}:1}"
# done
# done
# # Copy BPGD decimation passing
#
# echo -e "\rCopying BPGD param exploration results..."
# echo -n ' '
# for max_iter in 32 200 5000; do
# for F in 1 2 3; do
# for W in 3 4 5; do
# SRC_PATH="${BASE_PATH}+rust_exp=soft_v_hard_bpgd_pass_channel,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.max_iter=${max_iter},decoder.pass_soft_info=True,system.F=${F},system.W=${W}"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/WF/WindowingSyndromeSpaGdDecoderPassDecimation/max_iter_${max_iter}/pass_soft_info_True/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++%${#sp}:1}"
# done
# done
# done
# Copy BPGD with decimation info passing over max iter. results
echo -e "\rCopying BPGD over max. iter. results..."
echo -n ' '
for pass_soft_info in "True" "False"; do
for F in 1 2 3; do
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd_pass_channel,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=0.0025,system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoderPassDecimation/p_0.0025/pass_soft_info_${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

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,10000,0.0238,0.0020053029820819024,238.00000000000003
0.0015,4000,0.072,0.006207614833215747,288.0
0.002,2000,0.1545,0.013888248487303967,309.0
0.0025,2000,0.316,0.03115416825283479,632.0
0.003,2000,0.5005,0.05620437958483415,1000.9999999999999
0.0035,2000,0.6885,0.09262215539223495,1377.0
0.004,2000,0.8325,0.13834270815997607,1665.0
0.001,10000,0.0225,0.0018946185336699006,225.0
0.0015,4000,0.07025,0.006051577178537326,281.0
0.002,2000,0.1515,0.01359714508496701,303.0
0.0025,2000,0.317,0.03127228419350614,634.0
0.003,2000,0.494,0.05518696485931962,988.0
0.0035,2000,0.6805,0.09070269229398742,1361.0
0.004,2000,0.8305,0.1374899943124941,1661.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 10000 0.0238 0.0225 0.0020053029820819024 0.0018946185336699006 238.00000000000003 225.0
3 0.0015 4000 0.072 0.07025 0.006207614833215747 0.006051577178537326 288.0 281.0
4 0.002 2000 0.1545 0.1515 0.013888248487303967 0.01359714508496701 309.0 303.0
5 0.0025 2000 0.316 0.317 0.03115416825283479 0.03127228419350614 632.0 634.0
6 0.003 2000 0.5005 0.494 0.05620437958483415 0.05518696485931962 1000.9999999999999 988.0
7 0.0035 2000 0.6885 0.6805 0.09262215539223495 0.09070269229398742 1377.0 1361.0
8 0.004 2000 0.8325 0.8305 0.13834270815997607 0.1374899943124941 1665.0 1661.0

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@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,14000,0.0152142857142857,0.0012767850842765327,212.9999999999998
0.0015,6000,0.0423333333333333,0.003598136858263712,253.9999999999998
0.002,4000,0.09125,0.007942064662371462,365.0
0.001,14000,0.0149285714285714,0.0012526417987225313,208.9999999999996
0.0015,6000,0.0413333333333333,0.0035114743705089158,247.9999999999998
0.002,4000,0.09075,0.00789658974957197,363.0
0.0025,2000,0.2125,0.019710810011481006,425.0
0.003,2000,0.3495,0.03520004381148534,699.0
0.0035,2000,0.5385,0.062407102537387016,1077.0
0.004,2000,0.73,0.10336921268218224,1460.0
0.003,2000,0.3505,0.03532372820929974,701.0
0.0035,2000,0.545,0.06351473218845116,1090.0
0.004,2000,0.733,0.10420368457269413,1466.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 14000 0.0152142857142857 0.0149285714285714 0.0012767850842765327 0.0012526417987225313 212.9999999999998 208.9999999999996
3 0.0015 6000 0.0423333333333333 0.0413333333333333 0.003598136858263712 0.0035114743705089158 253.9999999999998 247.9999999999998
4 0.002 4000 0.09125 0.09075 0.007942064662371462 0.00789658974957197 365.0 363.0
5 0.0025 2000 0.2125 0.019710810011481006 425.0
6 0.003 2000 0.3495 0.3505 0.03520004381148534 0.03532372820929974 699.0 701.0
7 0.0035 2000 0.5385 0.545 0.062407102537387016 0.06351473218845116 1077.0 1090.0
8 0.004 2000 0.73 0.733 0.10336921268218224 0.10420368457269413 1460.0 1466.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.09525,0.00830669189113975,381.0
0.0015,2000,0.2055,0.018987611527110704,411.0
0.002,2000,0.357,0.03613195793118629,714.0
0.0025,2000,0.545,0.06351473218845116,1090.0
0.003,2000,0.6935,0.09384489827464226,1387.0
0.0035,2000,0.832,0.1381286582468193,1664.0
0.004,2000,0.93,0.19876821407276757,1860.0
0.001,4000,0.0955,0.008329530127408447,382.0
0.0015,2000,0.206,0.019039074473767514,412.0
0.002,2000,0.3575,0.0361944392516631,715.0
0.0025,2000,0.5435,0.06325784394063028,1087.0
0.003,2000,0.6975,0.09483632938531728,1395.0
0.0035,2000,0.8315,0.1379151915045972,1663.0
0.004,2000,0.931,0.19972836446856335,1862.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.09525 0.0955 0.00830669189113975 0.008329530127408447 381.0 382.0
3 0.0015 2000 0.2055 0.206 0.018987611527110704 0.019039074473767514 411.0 412.0
4 0.002 2000 0.357 0.3575 0.03613195793118629 0.0361944392516631 714.0 715.0
5 0.0025 2000 0.545 0.5435 0.06351473218845116 0.06325784394063028 1090.0 1087.0
6 0.003 2000 0.6935 0.6975 0.09384489827464226 0.09483632938531728 1387.0 1395.0
7 0.0035 2000 0.832 0.8315 0.1381286582468193 0.1379151915045972 1664.0 1663.0
8 0.004 2000 0.93 0.931 0.19876821407276757 0.19972836446856335 1860.0 1862.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,10000,0.0241,0.002030864734142268,241.0
0.0015,4000,0.06575,0.00565157033403707,263.0
0.002,2000,0.141,0.012585659483247746,282.0
0.0025,2000,0.2935,0.028537560287876573,587.0
0.001,10000,0.0235,0.00197974842986981,235.0
0.0015,4000,0.065,0.005585074297480008,260.0
0.002,2000,0.139,0.012394281484896852,278.0
0.0025,2000,0.29,0.028137416075114108,580.0
0.003,2000,0.4435,0.0476671526131055,887.0
0.0035,2000,0.6345,0.08045296488086273,1269.0
0.004,2000,0.8025,0.1264309108240147,1605.0
0.0035,2000,0.6325,0.08003470274679836,1265.0
0.004,2000,0.8015,0.12606316890291858,1603.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 10000 0.0241 0.0235 0.002030864734142268 0.00197974842986981 241.0 235.0
3 0.0015 4000 0.06575 0.065 0.00565157033403707 0.005585074297480008 263.0 260.0
4 0.002 2000 0.141 0.139 0.012585659483247746 0.012394281484896852 282.0 278.0
5 0.0025 2000 0.2935 0.29 0.028537560287876573 0.028137416075114108 587.0 580.0
6 0.003 2000 0.4435 0.0476671526131055 887.0
7 0.0035 2000 0.6345 0.6325 0.08045296488086273 0.08003470274679836 1269.0 1265.0
8 0.004 2000 0.8025 0.8015 0.1264309108240147 0.12606316890291858 1605.0 1603.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.07675,0.006632509273037823,307.0
0.0015,2000,0.158,0.014229068201835782,316.0
0.002,2000,0.2905,0.028194468681520868,581.0
0.0025,2000,0.4345,0.046393101582849816,869.0
0.003,2000,0.6165,0.07676176791425904,1233.0
0.001,4000,0.0755,0.0065205010146435205,302.0
0.0015,2000,0.1605,0.014473306861186974,321.0
0.002,2000,0.29,0.028137416075114108,580.0
0.0025,2000,0.4385,0.046957034683799304,877.0
0.003,2000,0.6175,0.07696262481687499,1235.0
0.0035,2000,0.7815,0.11904386007281431,1563.0
0.004,2000,0.902,0.17598426223523023,1804.0
0.004,2000,0.9015,0.1756347320107573,1803.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.07675 0.0755 0.006632509273037823 0.0065205010146435205 307.0 302.0
3 0.0015 2000 0.158 0.1605 0.014229068201835782 0.014473306861186974 316.0 321.0
4 0.002 2000 0.2905 0.29 0.028194468681520868 0.028137416075114108 581.0 580.0
5 0.0025 2000 0.4345 0.4385 0.046393101582849816 0.046957034683799304 869.0 877.0
6 0.003 2000 0.6165 0.6175 0.07676176791425904 0.07696262481687499 1233.0 1235.0
7 0.0035 2000 0.7815 0.11904386007281431 1563.0
8 0.004 2000 0.902 0.9015 0.17598426223523023 0.1756347320107573 1804.0 1803.0

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@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0351666666666666,0.0029788796512925098,210.99999999999963
0.001,6000,0.0356666666666666,0.0030219465890483077,213.99999999999957
0.0015,2000,0.102,0.008925364554660087,204.0
0.002,2000,0.22,0.02049223417716306,440.0
0.0025,2000,0.39,0.040354525526934304,780.0
0.003,2000,0.5675,0.06746420864522562,1135.0
0.0035,2000,0.7385,0.10575612450061989,1477.0
0.002,2000,0.2205,0.02054457368926077,441.0
0.0025,2000,0.389,0.040223524818782996,778.0
0.003,2000,0.567,0.06737441654139043,1134.0
0.0035,2000,0.7395,0.10604159775255029,1479.0
0.004,2000,0.8585,0.15037026489320615,1717.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0351666666666666 0.0356666666666666 0.0029788796512925098 0.0030219465890483077 210.99999999999963 213.99999999999957
3 0.0015 2000 0.102 0.008925364554660087 204.0
4 0.002 2000 0.22 0.2205 0.02049223417716306 0.02054457368926077 440.0 441.0
5 0.0025 2000 0.39 0.389 0.040354525526934304 0.040223524818782996 780.0 778.0
6 0.003 2000 0.5675 0.567 0.06746420864522562 0.06737441654139043 1135.0 1134.0
7 0.0035 2000 0.7385 0.7395 0.10575612450061989 0.10604159775255029 1477.0 1479.0
8 0.004 2000 0.8585 0.15037026489320615 1717.0

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@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.02775,0.0023424443475220658,222.0
0.0015,4000,0.072,0.006207614833215747,288.0
0.001,8000,0.028,0.002363824632785727,224.0
0.0015,4000,0.07175,0.00618530723400279,287.0
0.002,2000,0.1445,0.012921555968088194,289.0
0.0025,2000,0.2795,0.026947740549572385,559.0
0.003,2000,0.441,0.04731136584915907,882.0
0.0035,2000,0.6185,0.07716396365395395,1237.0
0.0025,2000,0.2805,0.027060355839749417,561.0
0.003,2000,0.442,0.04745350518327096,884.0
0.0035,2000,0.62,0.0774668808446417,1240.0
0.004,2000,0.788,0.12125812649764789,1576.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.02775 0.028 0.0023424443475220658 0.002363824632785727 222.0 224.0
3 0.0015 4000 0.072 0.07175 0.006207614833215747 0.00618530723400279 288.0 287.0
4 0.002 2000 0.1445 0.012921555968088194 289.0
5 0.0025 2000 0.2795 0.2805 0.026947740549572385 0.027060355839749417 559.0 561.0
6 0.003 2000 0.441 0.442 0.04731136584915907 0.04745350518327096 882.0 884.0
7 0.0035 2000 0.6185 0.62 0.07716396365395395 0.0774668808446417 1237.0 1240.0
8 0.004 2000 0.788 0.12125812649764789 1576.0

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@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0446666666666666,0.003800672363933444,267.9999999999996
0.001,6000,0.0443333333333333,0.0037717109761096212,265.99999999999983
0.0015,2000,0.1105,0.00971052637237435,221.0
0.002,2000,0.212,0.019658957946067646,424.0
0.0025,2000,0.384,0.03957145487337721,768.0
0.003,2000,0.5495,0.06429008164677474,1099.0
0.0035,2000,0.734,0.10448375252924946,1468.0
0.0025,2000,0.383,0.03944162334187884,766.0
0.003,2000,0.55,0.0643766693499016,1100.0
0.0035,2000,0.7345,0.10462414862704472,1469.0
0.004,2000,0.8555,0.14888354449188368,1711.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0446666666666666 0.0443333333333333 0.003800672363933444 0.0037717109761096212 267.9999999999996 265.99999999999983
3 0.0015 2000 0.1105 0.00971052637237435 221.0
4 0.002 2000 0.212 0.019658957946067646 424.0
5 0.0025 2000 0.384 0.383 0.03957145487337721 0.03944162334187884 768.0 766.0
6 0.003 2000 0.5495 0.55 0.06429008164677474 0.0643766693499016 1099.0 1100.0
7 0.0035 2000 0.734 0.7345 0.10448375252924946 0.10462414862704472 1468.0 1469.0
8 0.004 2000 0.8555 0.14888354449188368 1711.0

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@@ -3,6 +3,6 @@ physical_p,num_trials,LER,LER_per_round,num_errors
0.0015,2000,0.202,0.018628199928893086,404.0
0.002,2000,0.37,0.0377711386183186,740.0
0.0025,2000,0.512,0.05803452525767827,1024.0
0.003,2000,0.6965,0.0945873500188964,1393.0
0.003,2000,0.6975,0.09483632938531728,1395.0
0.0035,2000,0.834,0.13898839048277334,1668.0
0.004,2000,0.9305,0.19924670627472074,1861.0
1 physical_p num_trials LER LER_per_round num_errors
3 0.0015 2000 0.202 0.018628199928893086 404.0
4 0.002 2000 0.37 0.0377711386183186 740.0
5 0.0025 2000 0.512 0.05803452525767827 1024.0
6 0.003 2000 0.6965 0.6975 0.0945873500188964 0.09483632938531728 1393.0 1395.0
7 0.0035 2000 0.834 0.13898839048277334 1668.0
8 0.004 2000 0.9305 0.19924670627472074 1861.0

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@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00039,3.250581082292481e-05,39.0
0.0015,84000,0.0023809523809523,0.00019862955014460226,199.9999999999932
0.002,22000,0.0093181818181818,0.0007798513967101073,204.9999999999996
0.0025,6000,0.0348333333333333,0.002950179723826829,208.9999999999998
0.003,2000,0.102,0.008925364554660087,204.0
0.0035,2000,0.2065,0.01909056713578361,413.0
0.004,2000,0.37,0.0377711386183186,740.0
0.001,4000,0.0665,0.005718115322006723,266.0
0.0015,2000,0.163,0.014718213155383575,326.0
0.002,2000,0.3335,0.03324443774167962,667.0
0.0025,2000,0.535,0.06181659632516823,1070.0
0.003,2000,0.7185,0.10024721430418793,1437.0
0.0035,2000,0.849,0.14575703232266357,1698.0
0.004,2000,0.928,0.1968850543381443,1856.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 4000 0.00039 0.0665 3.250581082292481e-05 0.005718115322006723 39.0 266.0
3 0.0015 84000 2000 0.0023809523809523 0.163 0.00019862955014460226 0.014718213155383575 199.9999999999932 326.0
4 0.002 22000 2000 0.0093181818181818 0.3335 0.0007798513967101073 0.03324443774167962 204.9999999999996 667.0
5 0.0025 6000 2000 0.0348333333333333 0.535 0.002950179723826829 0.06181659632516823 208.9999999999998 1070.0
6 0.003 2000 0.102 0.7185 0.008925364554660087 0.10024721430418793 204.0 1437.0
7 0.0035 2000 0.2065 0.849 0.01909056713578361 0.14575703232266357 413.0 1698.0
8 0.004 2000 0.37 0.928 0.0377711386183186 0.1968850543381443 740.0 1856.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00012,1.0000550042188472e-05,12.0
0.0015,100000,0.00088,7.33629277510639e-05,88.0
0.002,38000,0.0052894736842105,0.0004418617234457356,200.999999999999
0.0025,10000,0.0219,0.0018435789073465436,219.0
0.003,4000,0.062,0.005319578163374583,248.0
0.0035,2000,0.131,0.01163282218493733,262.0
0.004,2000,0.257,0.024451045518294245,514.0
0.001,4000,0.05825,0.004988800426538265,233.0
0.0015,2000,0.15,0.013451947011868914,300.0
0.002,2000,0.305,0.0298652369138821,610.0
0.0025,2000,0.503,0.056598927298699975,1006.0
0.003,2000,0.6875,0.0923797676224748,1375.0
0.0035,2000,0.8255,0.13539790019792786,1651.0
0.004,2000,0.9,0.1745958147319816,1800.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 4000 0.00012 0.05825 1.0000550042188472e-05 0.004988800426538265 12.0 233.0
3 0.0015 100000 2000 0.00088 0.15 7.33629277510639e-05 0.013451947011868914 88.0 300.0
4 0.002 38000 2000 0.0052894736842105 0.305 0.0004418617234457356 0.0298652369138821 200.999999999999 610.0
5 0.0025 10000 2000 0.0219 0.503 0.0018435789073465436 0.056598927298699975 219.0 1006.0
6 0.003 4000 2000 0.062 0.6875 0.005319578163374583 0.0923797676224748 248.0 1375.0
7 0.0035 2000 0.131 0.8255 0.01163282218493733 0.13539790019792786 262.0 1651.0
8 0.004 2000 0.257 0.9 0.024451045518294245 0.1745958147319816 514.0 1800.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,98000,0.0020510204081632,0.00017107925018833292,200.9999999999936
0.0015,18000,0.0115,0.0009634219750452866,207.0
0.002,6000,0.0333333333333333,0.0028211423839156202,199.9999999999998
0.0025,4000,0.0985,0.0086040412348235,394.0
0.003,2000,0.197,0.01811725341494619,394.0
0.0035,2000,0.361,0.03663306068521166,722.0
0.004,2000,0.538,0.06232249368583764,1076.0
0.001,8000,0.02625,0.00221426836603833,210.0
0.0015,4000,0.0735,0.006341576227078316,294.0
0.002,2000,0.1445,0.012921555968088194,289.0
0.0025,2000,0.3035,0.029690924608377744,607.0
0.003,2000,0.4855,0.05387442629125816,971.0
0.0035,2000,0.65,0.08376754717420642,1300.0
0.004,2000,0.7795,0.11837469015649149,1559.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 98000 8000 0.0020510204081632 0.02625 0.00017107925018833292 0.00221426836603833 200.9999999999936 210.0
3 0.0015 18000 4000 0.0115 0.0735 0.0009634219750452866 0.006341576227078316 207.0 294.0
4 0.002 6000 2000 0.0333333333333333 0.1445 0.0028211423839156202 0.012921555968088194 199.9999999999998 289.0
5 0.0025 4000 2000 0.0985 0.3035 0.0086040412348235 0.029690924608377744 394.0 607.0
6 0.003 2000 0.197 0.4855 0.01811725341494619 0.05387442629125816 394.0 971.0
7 0.0035 2000 0.361 0.65 0.03663306068521166 0.08376754717420642 722.0 1300.0
8 0.004 2000 0.538 0.7795 0.06232249368583764 0.11837469015649149 1076.0 1559.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00024,2.0002200337376763e-05,24.0
0.0015,100000,0.00194,0.00016181059369868578,194.0
0.002,22000,0.009090909090909,0.000760750779222108,199.999999999998
0.0025,6000,0.0346666666666666,0.002935833167148738,207.9999999999996
0.003,4000,0.09,0.007828420342483211,360.0
0.0035,2000,0.187,0.01710405023748829,374.0
0.004,2000,0.3445,0.03458422416587814,689.0
0.001,10000,0.0205,0.00172459796871538,205.0
0.0015,4000,0.058,0.004966791530059078,232.0
0.002,2000,0.141,0.012585659483247746,282.0
0.0025,2000,0.2655,0.02538599006454134,531.0
0.003,2000,0.4425,0.047524662435589615,885.0
0.0035,2000,0.609,0.07527046293149453,1218.0
0.004,2000,0.7395,0.10604159775255029,1479.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 10000 0.00024 0.0205 2.0002200337376763e-05 0.00172459796871538 24.0 205.0
3 0.0015 100000 4000 0.00194 0.058 0.00016181059369868578 0.004966791530059078 194.0 232.0
4 0.002 22000 2000 0.009090909090909 0.141 0.000760750779222108 0.012585659483247746 199.999999999998 282.0
5 0.0025 6000 2000 0.0346666666666666 0.2655 0.002935833167148738 0.02538599006454134 207.9999999999996 531.0
6 0.003 4000 2000 0.09 0.4425 0.007828420342483211 0.047524662435589615 360.0 885.0
7 0.0035 2000 0.187 0.609 0.01710405023748829 0.07527046293149453 374.0 1218.0
8 0.004 2000 0.3445 0.7395 0.03458422416587814 0.10604159775255029 689.0 1479.0

View File

@@ -1,8 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00116,9.671809923239572e-05,116.0
0.0015,30000,0.0067,0.0005600552572093687,201.0
0.002,10000,0.0216,0.0018180698558927233,216.0
0.0025,4000,0.07625,0.006587689298086841,305.0
0.003,2000,0.151,0.01354871960160986,302.0
0.0035,2000,0.299,0.029170044726508193,598.0
0.004,2000,0.449,0.04845506933740451,898.0
0.001,16000,0.01325,0.0011109294970204076,212.0
0.0015,6000,0.0361666666666666,0.003065034000747535,216.99999999999957
0.002,4000,0.095,0.008283859438899643,380.0
0.0025,2000,0.1775,0.01615203373482954,355.0
0.003,2000,0.3365,0.03360781199094065,673.0
0.0035,2000,0.4985,0.055890042576412724,997.0
0.004,2000,0.671,0.08847974243602208,1342.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 16000 0.00116 0.01325 9.671809923239572e-05 0.0011109294970204076 116.0 212.0
3 0.0015 30000 6000 0.0067 0.0361666666666666 0.0005600552572093687 0.003065034000747535 201.0 216.99999999999957
4 0.002 10000 4000 0.0216 0.095 0.0018180698558927233 0.008283859438899643 216.0 380.0
5 0.0025 4000 2000 0.07625 0.1775 0.006587689298086841 0.01615203373482954 305.0 355.0
6 0.003 2000 0.151 0.3365 0.01354871960160986 0.03360781199094065 302.0 673.0
7 0.0035 2000 0.299 0.4985 0.029170044726508193 0.055890042576412724 598.0 997.0
8 0.004 2000 0.449 0.671 0.04845506933740451 0.08847974243602208 898.0 1342.0

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.2794452887537993,0.026941583361394406,1470.9999999999995
2048,5264,0.1073328267477203,0.009417167606857957,564.9999999999997
2560,5264,0.0381838905775075,0.00323907625680131,200.99999999999946
3072,5264,0.0381838905775075,0.00323907625680131,200.99999999999946
3584,5264,0.0381838905775075,0.00323907625680131,200.99999999999946
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 1536 5264 0.2794452887537993 0.026941583361394406 1470.9999999999995
8 2048 5264 0.1073328267477203 0.009417167606857957 564.9999999999997
9 2560 5264 0.0381838905775075 0.00323907625680131 200.99999999999946
10 3072 5264 0.0381838905775075 0.00323907625680131 200.99999999999946
11 3584 5264 0.0381838905775075 0.00323907625680131 200.99999999999946
12 4096 5264 0.0381838905775075 0.00323907625680131 200.99999999999946

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.2211246200607902,0.02061000156097803,1163.9999999999995
2048,5264,0.1988981762917933,0.018310882399525497,1047.0
2560,5264,0.0797872340425532,0.006905245826546502,420.0
3072,10528,0.0241261398176291,0.0020330923404081602,253.99999999999918
3584,10528,0.0241261398176291,0.0020330923404081602,253.99999999999918
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 1536 5264 0.2211246200607902 0.02061000156097803 1163.9999999999995
8 2048 5264 0.1988981762917933 0.018310882399525497 1047.0
9 2560 5264 0.0797872340425532 0.006905245826546502 420.0
10 3072 10528 0.0241261398176291 0.0020330923404081602 253.99999999999918
11 3584 10528 0.0241261398176291 0.0020330923404081602 253.99999999999918
12 4096 10528 0.0241261398176291 0.0020330923404081602 253.99999999999918

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.1890197568389057,0.017307768448815875,994.9999999999997
2048,5264,0.1903495440729483,0.01744214849069614,1001.9999999999998
2560,5264,0.1724924012158054,0.01565426054958796,907.9999999999995
3072,5264,0.1073328267477203,0.009417167606857957,564.9999999999997
3584,10528,0.0251709726443769,0.002122176652162411,265.0
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 1536 5264 0.1890197568389057 0.017307768448815875 994.9999999999997
8 2048 5264 0.1903495440729483 0.01744214849069614 1001.9999999999998
9 2560 5264 0.1724924012158054 0.01565426054958796 907.9999999999995
10 3072 5264 0.1073328267477203 0.009417167606857957 564.9999999999997
11 3584 10528 0.0251709726443769 0.002122176652162411 265.0
12 4096 10528 0.020991641337386 0.0017663630187246815 220.9999999999998

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.4791033434650456,0.05289972185893943,2522.0
2048,5264,0.2674772036474164,0.025604890903806354,1408.0
2560,5264,0.0921352583586626,0.008022635037369774,484.9999999999999
3072,5264,0.0921352583586626,0.008022635037369774,484.9999999999999
3584,5264,0.0921352583586626,0.008022635037369774,484.9999999999999
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 1536 5264 0.4791033434650456 0.05289972185893943 2522.0
8 2048 5264 0.2674772036474164 0.025604890903806354 1408.0
9 2560 5264 0.0921352583586626 0.008022635037369774 484.9999999999999
10 3072 5264 0.0921352583586626 0.008022635037369774 484.9999999999999
11 3584 5264 0.0921352583586626 0.008022635037369774 484.9999999999999
12 4096 5264 0.0921352583586626 0.008022635037369774 484.9999999999999

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.2767857142857143,0.026642791153438816,1457.0
2048,5264,0.2562689969604863,0.024371098470341246,1348.9999999999998
2560,5264,0.1187310030395136,0.0104774227315102,624.9999999999997
3072,10528,0.0359992401215805,0.00305060376648969,378.9999999999995
3584,10528,0.0359992401215805,0.00305060376648969,378.9999999999995
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 1536 5264 0.2767857142857143 0.026642791153438816 1457.0
8 2048 5264 0.2562689969604863 0.024371098470341246 1348.9999999999998
9 2560 5264 0.1187310030395136 0.0104774227315102 624.9999999999997
10 3072 10528 0.0359992401215805 0.00305060376648969 378.9999999999995
11 3584 10528 0.0359992401215805 0.00305060376648969 378.9999999999995
12 4096 10528 0.0359992401215805 0.00305060376648969 378.9999999999995

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.2159954407294833,0.020074146889335953,1137.0
2048,5264,0.2078267477203647,0.01922734698807005,1093.9999999999998
2560,5264,0.1778115501519757,0.01618309464777068,936.0
3072,5264,0.1225303951367781,0.010833635736165581,644.9999999999999
3584,10528,0.0296352583586626,0.0025037982429529926,311.9999999999999
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 1536 5264 0.2159954407294833 0.020074146889335953 1137.0
8 2048 5264 0.2078267477203647 0.01922734698807005 1093.9999999999998
9 2560 5264 0.1778115501519757 0.01618309464777068 936.0
10 3072 5264 0.1225303951367781 0.010833635736165581 644.9999999999999
11 3584 10528 0.0296352583586626 0.0025037982429529926 311.9999999999999
12 4096 10528 0.0237462006079027 0.0020007197211502348 249.9999999999996

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.910144376899696,0.18192063487808974,4790.999999999999
2048,5264,0.8763297872340425,0.15985272004411766,4613.0
2560,5264,0.8284574468085106,0.13662860463433912,4361.0
3072,5264,0.8284574468085106,0.13662860463433912,4361.0
3584,5264,0.8284574468085106,0.13662860463433912,4361.0
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 1536 5264 0.910144376899696 0.18192063487808974 4790.999999999999
8 2048 5264 0.8763297872340425 0.15985272004411766 4613.0
9 2560 5264 0.8284574468085106 0.13662860463433912 4361.0
10 3072 5264 0.8284574468085106 0.13662860463433912 4361.0
11 3584 5264 0.8284574468085106 0.13662860463433912 4361.0
12 4096 5264 0.8284574468085106 0.13662860463433912 4361.0

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.418693009118541,0.04419977411271481,2204.0
2048,5264,0.4046352583586626,0.042294622550035466,2130.0
2560,5264,0.2661474164133738,0.025457607509746905,1400.9999999999998
3072,5264,0.0773176291793313,0.0066834185002044855,406.99999999999994
3584,5264,0.0773176291793313,0.0066834185002044855,406.99999999999994
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 1536 5264 0.418693009118541 0.04419977411271481 2204.0
8 2048 5264 0.4046352583586626 0.042294622550035466 2130.0
9 2560 5264 0.2661474164133738 0.025457607509746905 1400.9999999999998
10 3072 5264 0.0773176291793313 0.0066834185002044855 406.99999999999994
11 3584 5264 0.0773176291793313 0.0066834185002044855 406.99999999999994
12 4096 5264 0.0773176291793313 0.0066834185002044855 406.99999999999994

View File

@@ -0,0 +1,12 @@
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
1536,5264,0.244870820668693,0.0231337521290782,1289.0
2048,5264,0.2420212765957446,0.022827091512257702,1273.9999999999995
2560,5264,0.2384118541033434,0.022440168236137703,1254.9999999999995
3072,5264,0.163563829787234,0.014773539998592322,860.9999999999998
3584,5264,0.0400835866261398,0.003403284629997727,210.9999999999999
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 1536 5264 0.244870820668693 0.0231337521290782 1289.0
8 2048 5264 0.2420212765957446 0.022827091512257702 1273.9999999999995
9 2560 5264 0.2384118541033434 0.022440168236137703 1254.9999999999995
10 3072 5264 0.163563829787234 0.014773539998592322 860.9999999999998
11 3584 5264 0.0400835866261398 0.003403284629997727 210.9999999999999
12 4096 10528 0.0310600303951367 0.0026259311852924183 326.9999999999992

View File

@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3850683890577507,0.03971037910801656,2026.9999999999998
128,5264,0.3187689969604863,0.03148161973097752,1677.9999999999998
256,5264,0.3077507598784194,0.03018579579258507,1619.9999999999995
512,5264,0.2995820668693009,0.029237246714529652,1576.9999999999998
1024,5264,0.2291033434650456,0.021450017503444885,1206.0
1536,5264,0.3221884498480243,0.03188767957701344,1696.0
2048,5264,0.3472644376899696,0.03492416878291982,1828.0
2560,5264,0.5324848024316109,0.061394752832159005,2802.9999999999995
3072,5264,0.5324848024316109,0.061394752832159005,2802.9999999999995
3584,5264,0.5324848024316109,0.061394752832159005,2802.9999999999995
4096,5264,0.5324848024316109,0.061394752832159005,2802.9999999999995
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3850683890577507 0.03971037910801656 2026.9999999999998
3 128 5264 0.3187689969604863 0.03148161973097752 1677.9999999999998
4 256 5264 0.3077507598784194 0.03018579579258507 1619.9999999999995
5 512 5264 0.2995820668693009 0.029237246714529652 1576.9999999999998
6 1024 5264 0.2291033434650456 0.021450017503444885 1206.0
7 1536 5264 0.3221884498480243 0.03188767957701344 1696.0
8 2048 5264 0.3472644376899696 0.03492416878291982 1828.0
9 2560 5264 0.5324848024316109 0.061394752832159005 2802.9999999999995
10 3072 5264 0.5324848024316109 0.061394752832159005 2802.9999999999995
11 3584 5264 0.5324848024316109 0.061394752832159005 2802.9999999999995
12 4096 5264 0.5324848024316109 0.061394752832159005 2802.9999999999995

View File

@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.2822948328267477,0.02726284179870908,1486.0
128,5264,0.2177051671732522,0.020252407127563266,1145.9999999999995
256,5264,0.2017477203647416,0.018602349442960464,1061.9999999999998
512,5264,0.1979483282674772,0.0182139378855688,1042.0
1024,5264,0.1631838905775076,0.014736254004048988,859.0
1536,5264,0.1800911854103343,0.016410698472618335,947.9999999999998
2048,5264,0.2974924012158054,0.02899622383582867,1565.9999999999995
2560,5264,0.3451747720364742,0.03466707988978823,1817.0
3072,5264,0.4967705167173252,0.055619147396563595,2615.0
3584,5264,0.4967705167173252,0.055619147396563595,2615.0
4096,5264,0.4967705167173252,0.055619147396563595,2615.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.2822948328267477 0.02726284179870908 1486.0
3 128 5264 0.2177051671732522 0.020252407127563266 1145.9999999999995
4 256 5264 0.2017477203647416 0.018602349442960464 1061.9999999999998
5 512 5264 0.1979483282674772 0.0182139378855688 1042.0
6 1024 5264 0.1631838905775076 0.014736254004048988 859.0
7 1536 5264 0.1800911854103343 0.016410698472618335 947.9999999999998
8 2048 5264 0.2974924012158054 0.02899622383582867 1565.9999999999995
9 2560 5264 0.3451747720364742 0.03466707988978823 1817.0
10 3072 5264 0.4967705167173252 0.055619147396563595 2615.0
11 3584 5264 0.4967705167173252 0.055619147396563595 2615.0
12 4096 5264 0.4967705167173252 0.055619147396563595 2615.0

View File

@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.2412613981762918,0.02274549383452562,1270.0
128,5264,0.184080547112462,0.016810404644448607,968.9999999999999
256,5264,0.175531914893617,0.01595606856502496,923.9999999999999
512,5264,0.1647036474164133,0.014885491207228485,866.9999999999997
1024,5264,0.1409574468085106,0.012581583360087745,741.9999999999998
1536,5264,0.116451367781155,0.010264370061932926,612.9999999999999
2048,5264,0.1852203647416413,0.016924935456301582,974.9999999999998
2560,5264,0.2931231003039514,0.02849438331179399,1543.0
3072,5264,0.3098404255319149,0.030430095656652667,1631.0
3584,5264,0.40330547112462,0.04211654648973906,2122.9999999999995
4096,5264,0.4549772036474164,0.049319566283534066,2395.0
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.2412613981762918 0.02274549383452562 1270.0
3 128 5264 0.184080547112462 0.016810404644448607 968.9999999999999
4 256 5264 0.175531914893617 0.01595606856502496 923.9999999999999
5 512 5264 0.1647036474164133 0.014885491207228485 866.9999999999997
6 1024 5264 0.1409574468085106 0.012581583360087745 741.9999999999998
7 1536 5264 0.116451367781155 0.010264370061932926 612.9999999999999
8 2048 5264 0.1852203647416413 0.016924935456301582 974.9999999999998
9 2560 5264 0.2931231003039514 0.02849438331179399 1543.0
10 3072 5264 0.3098404255319149 0.030430095656652667 1631.0
11 3584 5264 0.40330547112462 0.04211654648973906 2122.9999999999995
12 4096 5264 0.4549772036474164 0.049319566283534066 2395.0

View File

@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.5953647416413373,0.0726251577026028,3134.0
128,5264,0.5431231003039514,0.06319341818259305,2859.0
256,5264,0.5303951367781155,0.06104585785147554,2791.9999999999995
512,5264,0.5311550151975684,0.06117256378844538,2796.0
1024,5264,0.5026595744680851,0.056545094798418516,2646.0
1536,5264,0.4513677811550152,0.048796494007125824,2376.0
2048,5264,0.2623480243161094,0.02503814186021658,1380.9999999999998
2560,5264,0.297112462006079,0.02895247211574592,1563.9999999999998
3072,5264,0.297112462006079,0.02895247211574592,1563.9999999999998
3584,5264,0.297112462006079,0.02895247211574592,1563.9999999999998
4096,5264,0.297112462006079,0.02895247211574592,1563.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.5953647416413373 0.0726251577026028 3134.0
3 128 5264 0.5431231003039514 0.06319341818259305 2859.0
4 256 5264 0.5303951367781155 0.06104585785147554 2791.9999999999995
5 512 5264 0.5311550151975684 0.06117256378844538 2796.0
6 1024 5264 0.5026595744680851 0.056545094798418516 2646.0
7 1536 5264 0.4513677811550152 0.048796494007125824 2376.0
8 2048 5264 0.2623480243161094 0.02503814186021658 1380.9999999999998
9 2560 5264 0.297112462006079 0.02895247211574592 1563.9999999999998
10 3072 5264 0.297112462006079 0.02895247211574592 1563.9999999999998
11 3584 5264 0.297112462006079 0.02895247211574592 1563.9999999999998
12 4096 5264 0.297112462006079 0.02895247211574592 1563.9999999999998

View File

@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3765197568389057,0.03860492869161691,1981.9999999999995
128,5264,0.2946428571428571,0.02866861322375136,1550.9999999999998
256,5264,0.2767857142857143,0.026642791153438816,1457.0
512,5264,0.2712765957446808,0.02602705514677528,1427.9999999999998
1024,5264,0.2665273556231003,0.025499663499135905,1403.0
1536,5264,0.2562689969604863,0.024371098470341246,1348.9999999999998
2048,5264,0.1667933130699088,0.015091099283021325,877.9999999999999
2560,5264,0.1476063829787234,0.013220733692390962,776.9999999999999
3072,5264,0.2625379939209726,0.025059068054687383,1381.9999999999998
3584,5264,0.2625379939209726,0.025059068054687383,1381.9999999999998
4096,5264,0.2625379939209726,0.025059068054687383,1381.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3765197568389057 0.03860492869161691 1981.9999999999995
3 128 5264 0.2946428571428571 0.02866861322375136 1550.9999999999998
4 256 5264 0.2767857142857143 0.026642791153438816 1457.0
5 512 5264 0.2712765957446808 0.02602705514677528 1427.9999999999998
6 1024 5264 0.2665273556231003 0.025499663499135905 1403.0
7 1536 5264 0.2562689969604863 0.024371098470341246 1348.9999999999998
8 2048 5264 0.1667933130699088 0.015091099283021325 877.9999999999999
9 2560 5264 0.1476063829787234 0.013220733692390962 776.9999999999999
10 3072 5264 0.2625379939209726 0.025059068054687383 1381.9999999999998
11 3584 5264 0.2625379939209726 0.025059068054687383 1381.9999999999998
12 4096 5264 0.2625379939209726 0.025059068054687383 1381.9999999999998

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@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3183890577507599,0.031436617365659836,1676.0
128,5264,0.2232142857142857,0.020829240593055243,1175.0
256,5264,0.2104863221884498,0.01950216695467022,1107.9999999999998
512,5264,0.2068768996960486,0.019129401936932466,1089.0
1024,5264,0.1962386018237082,0.018039702664670587,1033.0
1536,5264,0.1876899696048632,0.017173590239868086,987.9999999999999
2048,5264,0.1386778115501519,0.01236348971224388,729.9999999999997
2560,5264,0.133548632218845,0.011874707191563583,703.0000000000001
3072,5264,0.130129179331307,0.011550323574472054,685.0
3584,5264,0.2042173252279635,0.018855726724976818,1074.9999999999998
4096,5264,0.2444908814589665,0.02309280281892645,1286.9999999999995
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3183890577507599 0.031436617365659836 1676.0
3 128 5264 0.2232142857142857 0.020829240593055243 1175.0
4 256 5264 0.2104863221884498 0.01950216695467022 1107.9999999999998
5 512 5264 0.2068768996960486 0.019129401936932466 1089.0
6 1024 5264 0.1962386018237082 0.018039702664670587 1033.0
7 1536 5264 0.1876899696048632 0.017173590239868086 987.9999999999999
8 2048 5264 0.1386778115501519 0.01236348971224388 729.9999999999997
9 2560 5264 0.133548632218845 0.011874707191563583 703.0000000000001
10 3072 5264 0.130129179331307 0.011550323574472054 685.0
11 3584 5264 0.2042173252279635 0.018855726724976818 1074.9999999999998
12 4096 5264 0.2444908814589665 0.02309280281892645 1286.9999999999995

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@@ -0,0 +1,12 @@
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
1536,5264,0.910144376899696,0.18192063487808974,4790.999999999999
2048,5264,0.8763297872340425,0.15985272004411766,4613.0
2560,5264,0.8284574468085106,0.13662860463433912,4361.0
3072,5264,0.8284574468085106,0.13662860463433912,4361.0
3584,5264,0.8284574468085106,0.13662860463433912,4361.0
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 1536 5264 0.910144376899696 0.18192063487808974 4790.999999999999
8 2048 5264 0.8763297872340425 0.15985272004411766 4613.0
9 2560 5264 0.8284574468085106 0.13662860463433912 4361.0
10 3072 5264 0.8284574468085106 0.13662860463433912 4361.0
11 3584 5264 0.8284574468085106 0.13662860463433912 4361.0
12 4096 5264 0.8284574468085106 0.13662860463433912 4361.0

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@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.5298252279635258,0.060950951675050935,2789.0
128,5264,0.4523176291793313,0.04893383806510265,2381.0
256,5264,0.4544072948328267,0.049236765210533884,2392.0
512,5264,0.4450987841945288,0.047895451992423954,2342.9999999999995
1024,5264,0.4236322188449848,0.04487918892421794,2230.0
1536,5264,0.4116641337386018,0.04324198215552311,2166.9999999999995
2048,5264,0.3958966565349544,0.0411310182640684,2084.0
2560,5264,0.2315729483282674,0.021711637892541602,1218.9999999999995
3072,5264,0.1852203647416413,0.016924935456301582,974.9999999999998
3584,5264,0.1852203647416413,0.016924935456301582,974.9999999999998
4096,5264,0.1852203647416413,0.016924935456301582,974.9999999999998
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.5298252279635258 0.060950951675050935 2789.0
3 128 5264 0.4523176291793313 0.04893383806510265 2381.0
4 256 5264 0.4544072948328267 0.049236765210533884 2392.0
5 512 5264 0.4450987841945288 0.047895451992423954 2342.9999999999995
6 1024 5264 0.4236322188449848 0.04487918892421794 2230.0
7 1536 5264 0.4116641337386018 0.04324198215552311 2166.9999999999995
8 2048 5264 0.3958966565349544 0.0411310182640684 2084.0
9 2560 5264 0.2315729483282674 0.021711637892541602 1218.9999999999995
10 3072 5264 0.1852203647416413 0.016924935456301582 974.9999999999998
11 3584 5264 0.1852203647416413 0.016924935456301582 974.9999999999998
12 4096 5264 0.1852203647416413 0.016924935456301582 974.9999999999998

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@@ -0,0 +1,12 @@
max_iter,num_trials,LER,LER_per_round,num_errors
32,5264,0.3782294832826747,0.038824902532404226,1990.9999999999995
128,5264,0.2661474164133738,0.025457607509746905,1400.9999999999998
256,5264,0.2513297872340425,0.023832795657756978,1322.9999999999995
512,5264,0.2416413373860182,0.022786283304920496,1271.9999999999998
1024,5264,0.2353723404255319,0.022115639689234468,1239.0
1536,5264,0.2344224924012158,0.022014467014769612,1234.0
2048,5264,0.2349924012158054,0.022075156802095708,1236.9999999999995
2560,5264,0.1914893617021276,0.017557492508172845,1007.9999999999997
3072,5264,0.1274696048632219,0.011298832322427566,671.0000000000001
3584,5264,0.1289893617021276,0.011442455421834419,678.9999999999997
4096,5264,0.1508358662613981,0.013532828789684093,793.9999999999997
1 max_iter num_trials LER LER_per_round num_errors
2 32 5264 0.3782294832826747 0.038824902532404226 1990.9999999999995
3 128 5264 0.2661474164133738 0.025457607509746905 1400.9999999999998
4 256 5264 0.2513297872340425 0.023832795657756978 1322.9999999999995
5 512 5264 0.2416413373860182 0.022786283304920496 1271.9999999999998
6 1024 5264 0.2353723404255319 0.022115639689234468 1239.0
7 1536 5264 0.2344224924012158 0.022014467014769612 1234.0
8 2048 5264 0.2349924012158054 0.022075156802095708 1236.9999999999995
9 2560 5264 0.1914893617021276 0.017557492508172845 1007.9999999999997
10 3072 5264 0.1274696048632219 0.011298832322427566 671.0000000000001
11 3584 5264 0.1289893617021276 0.011442455421834419 678.9999999999997
12 4096 5264 0.1508358662613981 0.013532828789684093 793.9999999999997