86 Commits

Author SHA1 Message Date
17191382cf Incorporate Lia's corrections to QM and QEC fundamentals 2026-05-04 13:01:54 +02:00
aa907ef4a3 Incorporate Lia's corrections to classical fundamentals 2026-05-04 12:12:10 +02:00
12036caa91 Fix bibliography titlecase (in clean_bibliography.sh) and a few things in the bibliography itself 2026-05-04 10:53:50 +02:00
4c206ae9c4 Rephrase first sentence of abstract 2026-05-04 10:34:53 +02:00
01a754e5da Reset acronyms after abstract 2026-05-04 10:31:54 +02:00
81292a2644 Add abstract 2026-05-04 10:28:46 +02:00
73958d7850 Check out version of cel thesis template with option for signature 2026-05-04 02:08:32 +02:00
18e3683502 Add signature 2026-05-04 02:06:58 +02:00
1eb4db289e Make main.tex work with signature modification 2026-05-04 02:06:39 +02:00
f56cd05890 Fix bibtex definition for arxiv papers 2026-05-04 01:45:46 +02:00
e9d996155d Write conclusion 2026-05-04 01:21:26 +02:00
5e26179154 Finish intro 2026-05-03 20:51:32 +02:00
9ae98e07d7 Write most of Introduction; Fix citing Intro. 2026-05-03 19:09:29 +02:00
728c8560c7 Fix N_C/N_V notation 2026-05-03 14:07:04 +02:00
dd30b4fc0d Write captions 2026-05-03 04:26:58 +02:00
6e53ed5d1b Complete results chapter text 2026-05-03 04:00:05 +02:00
0016df0004 Add text for second BPGD plot 2026-05-03 03:10:21 +02:00
9ca2698d38 Add text for first BPGD figure 2026-05-03 02:16:22 +02:00
72461fe555 Complete first draft of warm-start sliding-window decoding section 2026-05-03 01:11:22 +02:00
5fabe2e146 Finish first draft of BP warm start subsection 2026-05-02 23:40:29 +02:00
a90458dd8a Write conclusion to BP investigation. BP investigation now done 2026-05-02 19:16:26 +02:00
d2960b8f0e Write text for figure 4.10 2026-05-02 17:59:44 +02:00
6b1821fd6b Add TODOs; Add magnified plot to other figure 2026-05-02 17:28:20 +02:00
5687499b5b Add paragraphs exp. params, description, and interpretation for fig. 4.9 2026-05-02 16:58:02 +02:00
f4718b67e7 Complete text for first two figures 2026-05-02 14:38:50 +02:00
0848e5dea6 Move order of figures 2026-05-02 11:38:04 +02:00
7e18985b86 Add whole decoding line to max_iter plot 2026-05-02 11:22:25 +02:00
152e784546 Refactor the intro to numerical results 2026-05-02 10:38:51 +02:00
15190ccf48 Include claude corrections for first 5 pages of decoding chapter 2026-05-02 09:01:19 +02:00
606d68e2c1 Rephrase to remove 'gate schedule' 2026-05-01 21:56:03 +02:00
47493a6beb Write numerical results intro 2026-05-01 21:51:48 +02:00
76a91c7d32 Add H_overlap to figure 2026-05-01 19:33:37 +02:00
1632f19c47 Add syndrome update equation 2026-05-01 19:31:18 +02:00
3d3556689e Add node set Visualization figure 2026-05-01 19:13:22 +02:00
4555570665 Finish index definitions 2026-05-01 18:08:57 +02:00
3b7618e1d1 Start VN and CN indexing from zero 2026-05-01 17:30:14 +02:00
635c0aab18 Rewrite VN and CN set definition text; Fix earlier TODOs 2026-05-01 16:43:16 +02:00
c555151b9d Wwrite a few paragraphs on the window generation/decoding 2026-05-01 11:47:21 +02:00
05348579f0 Switch figures 2026-05-01 10:50:07 +02:00
1059b4d98f Add QUITS paper to review 2026-05-01 10:41:54 +02:00
682eeb644e Add literature overview figure 2026-04-30 19:59:21 +02:00
27f13c1db0 Write chapter 4 intro 2026-04-30 14:05:32 +02:00
8071c9f485 Fix typos 2026-04-29 21:03:26 +02:00
94e4c9f8c9 Replace autoref by cref 2026-04-29 20:56:41 +02:00
64cf0e2269 Introduce LER 2026-04-29 20:31:41 +02:00
76270695b9 Fix notation 2026-04-29 20:22:26 +02:00
62b4d4838b Write stim subsection 2026-04-29 18:24:21 +02:00
0aa425ae41 Polish per-round logical error rate subsection 2026-04-29 17:40:37 +02:00
b73a66649c Rewrite DEM subsection; Write first draft of practical considerations 2026-04-29 16:12:25 +02:00
d7f05dc5b9 Rework detector matrix and detector error matrix sections 2026-04-29 13:26:35 +02:00
11178436b6 Rewrite parts of measurement syndrome matrix subsection 2026-04-29 09:26:41 +02:00
dc283012ba Move figures in chapter 3 inbetween text 2026-04-28 23:45:44 +02:00
87e48b5ac6 Move 3-qubit repetition code check matrix; Rewrite DEM intro 2026-04-28 18:58:16 +02:00
42a689d811 Polish second paragraphs of noise model subsecions 2026-04-28 18:12:21 +02:00
b46df8120b Rewrite intro to chapter 3; Add subsections for each noise model 2026-04-28 16:22:03 +02:00
5ced7b152e Write first couple of pages of chapter 3 2026-04-28 01:59:52 +02:00
3953320216 Write mathematical fault tolerance definition 2026-04-27 18:03:28 +02:00
4aa4799969 Fix wrong sim results; Add bpgd with decimation info passing over max iter plots 2026-04-27 16:05:54 +02:00
f899942029 Add TODOs 2026-04-27 00:26:08 +02:00
a68e22d7f5 Add simulation results for all investigations 2026-04-27 00:21:59 +02:00
0955cdd14e Add vanilla BP figures 2026-04-26 19:28:03 +02:00
7015f9d644 Add TODOs to fault tolerance chapter 2026-04-25 21:58:00 +02:00
b50308d014 Add a bunch of content TODOs 2026-04-25 19:35:53 +02:00
93f310d843 Add bit-flip noise and modify phenomenological and circuit-level noise figures 2026-04-25 18:49:03 +02:00
163ef926e7 Move figures to next chapter 2026-04-25 17:40:43 +02:00
4da37dbddc Add three-qubit rep. code syndrome extraction circuit under bit-flip noise 2026-04-25 17:28:17 +02:00
6de9cec27e Add Tanner graph windowing figures with highlighted passed information 2026-04-25 17:09:04 +02:00
474b1d21da Add Tanner graph windowing figure 2026-04-25 16:00:19 +02:00
5483a972f9 Add windowing figure 2026-04-25 15:38:32 +02:00
5d104fbf28 Add detector construction figure 2026-04-25 15:13:02 +02:00
50a10ccb4f Add repeated syndrome extraction circuit figures for bit-flip and phenomenological noise 2026-04-25 14:59:26 +02:00
569df381ee Add clean_bibliography.sh; Incorporate LLM corrections 2026-04-25 14:14:58 +02:00
85771405db Add noise model figures 2026-04-25 14:14:41 +02:00
5875066581 Remove TODOs, formatting, minor changes 2026-04-24 17:58:30 +02:00
494a639329 Finish first draft of text for fundamentals 2026-04-24 17:44:16 +02:00
e59120b683 Fix unicode character in bib file 2026-04-24 14:16:18 +02:00
267d431542 Write BB code paragraph 2026-04-24 14:16:02 +02:00
4e1bd62504 Write CSS codes section 2026-04-24 11:04:57 +02:00
ada6e43be3 Finish writing stabilizer codes 2026-04-24 10:36:59 +02:00
6ea151ffeb Add backlog problem explanation 2026-04-24 09:25:55 +02:00
6e2cf5b8ba Add general syndrome extraction circuit 2026-04-24 00:36:34 +02:00
e792141afd Switch order of challenges 2026-04-23 13:06:01 +02:00
1810ec8632 Fix {ll,rr}bracket; Introduce Pauli group 2026-04-22 23:02:12 +02:00
513eb7579f Finish quantum circuits subsection 2026-04-22 22:48:08 +02:00
47c725e1fa Add Mai Anh's corrections 2026-04-22 22:19:15 +02:00
7d92b54deb Add Jonathan's corrections; n->3600 2026-04-22 20:41:09 +02:00
423 changed files with 9185 additions and 1012 deletions

View File

@@ -302,8 +302,8 @@
\item Quantum systems are inherently fragile \item Quantum systems are inherently fragile
\item Interacting with the quantum state disturbs it \item Interacting with the quantum state disturbs it
\item Idea: Represent $k\in \mathbb{N} $ \schlagwort{logical \item Idea: Represent $k\in \mathbb{N} $ \schlagwort{logical
qubits} using $n \in \mathbb{N},~n>k$ \schlagwort{physical qubits} qubits} using $n \in \mathbb{N}$ \schlagwort{physical qubits},
\citereferencemanual{Rof19} $n>k$ \citereferencemanual{Rof19}
\vspace*{2mm} \vspace*{2mm}
@@ -1532,8 +1532,8 @@
\item \schlagwort{Detector error model} (DEM) combines \item \schlagwort{Detector error model} (DEM) combines
detector error matrix and noise model detector error matrix and noise model
\visible<2->{ \visible<2->{
\item Tanner graph of detector error matrix of \ac{bb} code \item Tanner graph of detector error matrix of bivariate
\citereferencemanual{KSW$^+$25} bicycle (\acs{bb}) code \citereferencemanual{KSW$^+$25}
} }
\end{itemize} \end{itemize}
@@ -1549,21 +1549,25 @@
\vspace*{-5mm} \vspace*{-5mm}
\visible<3->{ \begin{itemize}
\begin{itemize} \visible<2->{
\item Challenges \item Challenges
\begin{itemize} }
\begin{itemize}
\visible<2->{
\item Fault tolerance: Additional error locations \\
$\implies$ \schlagwort{Increased decoding
complexity} \citereferencemanual{GCR24}
}
\visible<3->{
\item Quantum setting: Degeneracy and short \item Quantum setting: Degeneracy and short
cycles \\ cycles \\
$\implies$ \schlagwort{Degraded performance} $\implies$ \schlagwort{Degraded performance}
of belief propagation (BP) of belief propagation (BP)
\citereferencemanual{BBA$^+$15} \citereferencemanual{BBA$^+$15}
\item Fault tolerance: Additional error locations \\ }
$\implies$ \schlagwort{Increased decoding \end{itemize}
complexity} \citereferencemanual{GCR24} \end{itemize}
\end{itemize}
\end{itemize}
}
\vspace*{8mm} \vspace*{8mm}
@@ -1572,9 +1576,8 @@
S. Koutsioumpas et al., ``Automorphism ensemble decoding of S. Koutsioumpas et al., ``Automorphism ensemble decoding of
quantum LDPC codes,'' \emph{arXiv:2503.01738}, 2025. quantum LDPC codes,'' \emph{arXiv:2503.01738}, 2025.
} }
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
low-latency iterative decoding of qLDPC codes under of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
circuit-level noise,'' arXiv:2403.18901, 2024.
} }
{BBA$^+$15}{ {BBA$^+$15}{
Z. Babar et al., ``Fifteen years of Z. Babar et al., ``Fifteen years of
@@ -1634,9 +1637,8 @@
% S. Huang and S. Puri, ``Improved noisy syndrome decoding of % S. Huang and S. Puri, ``Improved noisy syndrome decoding of
% quantum LDPC codes with sliding window,'' \emph{arXiv:2311.03307}, 2023. % quantum LDPC codes with sliding window,'' \emph{arXiv:2311.03307}, 2023.
% } % }
% {GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward % {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
% low-latency iterative decoding of qLDPC codes under % of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
% circuit-level noise,'' arXiv:2403.18901, 2024.
% } % }
% {RWB$^+$20}{ % {RWB$^+$20}{
% J. Roffe et al., ``Decoding across the quantum low-density % J. Roffe et al., ``Decoding across the quantum low-density
@@ -1734,9 +1736,8 @@
S. Huang and S. Puri, ``Improved noisy syndrome decoding of S. Huang and S. Puri, ``Improved noisy syndrome decoding of
quantum LDPC codes with sliding window,'' \emph{arXiv:2311.03307}, 2023. quantum LDPC codes with sliding window,'' \emph{arXiv:2311.03307}, 2023.
} }
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
low-latency iterative decoding of qLDPC codes under of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
circuit-level noise,'' arXiv:2403.18901, 2024.
} }
\stopreferencesmanual \stopreferencesmanual
\end{frame} \end{frame}
@@ -2882,11 +2883,12 @@
\vspace*{-10mm} \vspace*{-10mm}
\centering \centering
\begin{itemize} \begin{itemize}
\only<1>{\vspace*{10mm}} \only<1>{\vspace*{10mm}}
\item Most errors due to non-convergence \item Most errors due to non-convergence
\vspace*{10mm} \vspace*{10mm}
\visible<2-> { \visible<2-> {
\item BPGD algorithm \citereferencemanual{YLH+24} \item BP with guided decimation (BPGD)
\citereferencemanual{YLH+24}
\begin{enumerate} \begin{enumerate}
\item Perform $T$ \schlagwort{BP iterations} \item Perform $T$ \schlagwort{BP iterations}
\item Hard decision on \schlagwort{most \item Hard decision on \schlagwort{most
@@ -2906,12 +2908,12 @@
\vspace*{-10mm} \vspace*{-10mm}
\begin{itemize} \begin{itemize}
\item $[[882, 24, 18 \le d \le 24]]$ - generalized \item $\llbracket 882, 24, 18 \le d \le 24
hypergraph product (GHP) code, \\ \rrbracket$ generalized hypergraph product (GHP) code,
bit-flip noise \citereferencemanual{YLH+24} bit-flip noise \citereferencemanual{YLH+24}
\end{itemize} \end{itemize}
\vspace*{-5mm} % \vspace*{-5mm}
\begin{figure}[H] \begin{figure}[H]
\centering \centering
@@ -2971,7 +2973,7 @@
} }
\end{minipage} \end{minipage}
\vspace*{2mm} \vspace*{5mm}
\addreferencesmanual \addreferencesmanual
{YLH+24}{Hanwen Yao et al. ``Belief propagation decoding of quantum {YLH+24}{Hanwen Yao et al. ``Belief propagation decoding of quantum
@@ -3329,7 +3331,7 @@
\item Parameters \item Parameters
\begin{itemize} \begin{itemize}
\item $T = 1$ \item $T = 1$
\item $n_\text{iterations} = n$ \item $n_\text{iterations} = 3{,}600$
\item $W = 5$ \item $W = 5$
\end{itemize} \end{itemize}
\end{itemize} \end{itemize}
@@ -3885,9 +3887,8 @@
\vspace*{15mm} \vspace*{15mm}
\addreferencesmanual \addreferencesmanual
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
low-latency iterative decoding of qLDPC codes under of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
circuit-level noise,'' arXiv:2403.18901, 2024.
} }
{MSL$^+$25}{ {MSL$^+$25}{
S. Miao et al., ``Quaternary neural belief propagation S. Miao et al., ``Quaternary neural belief propagation
@@ -4107,9 +4108,8 @@
\vspace*{30mm} \vspace*{30mm}
\addreferencesmanual \addreferencesmanual
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
low-latency iterative decoding of qLDPC codes under of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
circuit-level noise,'' arXiv:2403.18901, 2024.
} }
\stopreferencesmanual \stopreferencesmanual
\end{frame} \end{frame}
@@ -4170,9 +4170,8 @@
\vspace*{5mm} \vspace*{5mm}
\addreferencesmanual \addreferencesmanual
{GCR24}{A. Gong, S. Cammerer, and J. M. Renes, ``Toward {GCR24}{A. Gong et al., ``Toward low-latency iterative decoding
low-latency iterative decoding of qLDPC codes under of qLDPC codes under circuit-level noise,'' arXiv:2403.18901, 2024.
circuit-level noise,'' arXiv:2403.18901, 2024.
} }
\stopreferencesmanual \stopreferencesmanual
\end{frame} \end{frame}

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@@ -3,21 +3,61 @@
long=quantum error correction long=quantum error correction
} }
\DeclareAcronym{dem}{
short=DEM,
long=detector error model
}
\DeclareAcronym{ler}{
short=LER,
long=logical error rate
}
\DeclareAcronym{bp}{ \DeclareAcronym{bp}{
short=BP, short=BP,
long=belief propagation long=belief propagation
} }
\DeclareAcronym{bpgd}{
short=BPGD,
long=belief propagation with guided decimation
}
\DeclareAcronym{gdg}{
short=GDG,
long=guided decimation guessing
}
\DeclareAcronym{nms}{ \DeclareAcronym{nms}{
short=NMS, short=NMS,
long=normalized min-sum long=normalized min-sum
} }
\DeclareAcronym{osd}{
short=OSD,
long=ordered statistics decoding
}
\DeclareAcronym{aed}{
short=AED,
long=automorphism ensemble decoding
}
\DeclareAcronym{bsc}{
short=BSC,
long=binary symetric channel
}
\DeclareAcronym{spa}{ \DeclareAcronym{spa}{
short=SPA, short=SPA,
long=sum-product algorithm long=sum-product algorithm
} }
\DeclareAcronym{css}{
short=CSS,
long=Calderbank-Shor-Steane
}
\DeclareAcronym{llr}{ \DeclareAcronym{llr}{
short=LLR, short=LLR,
long=log-likelihood ratio long=log-likelihood ratio
@@ -33,6 +73,11 @@
long=low-density parity-check long=low-density parity-check
} }
\DeclareAcronym{qldpc}{
short=QLDPC,
long=quantum low-density parity-check
}
\DeclareAcronym{ml}{ \DeclareAcronym{ml}{
short=ML, short=ML,
long=maximum likelihood long=maximum likelihood
@@ -77,3 +122,23 @@
short=PDF, short=PDF,
long=probability density function long=probability density function
} }
\DeclareAcronym{bb}{
short=BB,
long=bivariate bicycle
}
\DeclareAcronym{hgp}{
short=HGP,
long=hypergraph product
}
\DeclareAcronym{lp}{
short=LP,
long=lifted-product
}
\DeclareAcronym{bpc}{
short=BPC,
long=balanced product code
}

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@@ -1 +1,197 @@
\chapter{Introduction} \chapter{Introduction}
\label{ch:Introduction}
\acresetall
% Intro to quantum computing
In 1982, Richard Feynman, motivated by the difficulty of simulating
quantum-mechanical systems on classical hardware, put forward the
idea of building computers that are themselves quantum mechanical
\cite{feynman_simulating_1982}.
The use of such quantum computers has since been shown to offer promising
prospects not only with regard to simulating quantum systems but also
for solving certain kinds of problems that are classically intractable.
The most prominent example is Shor's algorithm for integer
factorization \cite{shor_algorithms_1994}.
Similar to the way classical computers are built from bits and gates,
quantum computers are built from \emph{qubits} and \emph{quantum gates}.
Because of quantum entanglement, it is not enough to consider the
qubits individually, we also have to consider correlations between them.
For a system of $n$ qubits, this makes the state space grow with
$2^n$ instead of linearly with $n$, as would be the case for a classical system
\cite[Sec.~1]{gottesman_stabilizer_1997}.
This is both the reason quantum systems are difficult to simulate and
what provides them with their power \cite[Sec.~2.1]{roffe_decoding_2020}.
% The need for QEC
Realizing algorithms that leverage these quantum-mechanical effects
requires hardware that can execute long quantum computations reliably.
This poses a problem, because the qubits making up current devices
are difficult to sufficiently isolate from their environment
\cite[Sec.~1]{roffe_quantum_2019}.
Their interaction with the environment acts as a continuous small-scale
measurement, an effect we call \emph{decoherence} of the stored quantum
state.
Decoherence is the reason large systems don't exhibit visible quantum
properties at human scales \cite[Sec.~1]{gottesman_stabilizer_1997}.
% Intro to QEC
\Ac{qec} has emerged as a leading candidate in solving this problem.
It addresses the issue by encoding the information of $k$
\emph{logical qubits} into a larger number $n>k$ of \emph{physical
qubits}, in close analogy to classical channel coding
\cite[Sec.~1]{roffe_quantum_2019}.
The redundancy introduced this way can then be used to restore
the quantum state, should it be disturbed.
The quantum setting imposes some important constraints that do not exist in the
classical case, however \cite[Sec.~2.4]{roffe_quantum_2019}:
\begin{itemize}
\item The no-cloning theorem prohibits the duplication of quantum states.
\item In addition to the bit-flip errors we know from the
classical setting, qubits are subject to \emph{phase-flips}.
\item We are not allowed to directly measure the encoded qubits,
as that would disturb their quantum states.
\end{itemize}
We can deal with the first constraint by not duplicating information, instead
spreading the quantum state across the physical qubits
\cite[Sec.~I]{calderbank_good_1996}.
To deal with phase-flip errors, we must take special care when
constructing \ac{qec} codes.
Using \ac{css} codes, for example, we can use two separate classical
binary linear codes to protect against the two kinds of errors
\cite[Sec. 10.5.6]{nielsen_quantum_2010}.
Finally, we can get around the last issue by using \emph{stabilizer
measurements}.
These are parity measurements that give us information about
potential errors without revealing the underlying qubit states
\cite[Sec.~II.C.]{babar_fifteen_2015}.
This way, we perform a \emph{syndrome extraction} and base the
subsequent decoding process on the measured syndrome.
Another difference between \ac{qec} and classical channel coding is
the resource constraints.
For \ac{qec}, low latency matters more than low overall computational
complexity, due to the backlog problem
\cite[Sec.~II.G.3.]{terhal_quantum_2015}: Certain gates turn
single-qubit errors into multi-qubit ones, so errors must be
corrected beforehand.
A \ac{qec} system that is too slow accumulates a backlog at these points,
causing exponential slowdown.
Several code constructions have been proposed for \ac{qec} codes over the years.
Topological codes such as surface codes have been the industry
standard for experimental applications for a long time
\cite[Sec.~I]{koutsioumpas_colour_2025}, due to their
reliance on only local connections between qubits
\cite[Sec.~5]{roffe_decoding_2020}.
Recently, \ac{qldpc} codes have been getting increasing
attention as they have been shown to offer comparable thresholds with
substantially improved encoding rates \cite[Sec.~1]{bravyi_high-threshold_2024}.
\ac{qldpc} codes are generally decoded using a syndrome-based variant
of the \ac{bp} algorithm \cite[Sec.~1]{roffe_decoding_2020}.
We focus on \ac{qldpc} codes in our work and specifically \ac{bb} codes,
as they are promising candidates for practical QEC due to their high
encoding rates, large minimum distances, and short-depth syndrome
extraction circuits \cite[Sec.~1]{bravyi_high-threshold_2024}.
% DEMs and fault tolerance
The syndrome extraction itself is implemented on quantum hardware and
is therefore subject to the same noise as the data qubits.
As a consequence, the \ac{qec} procedure, meant to protect the quantum
state, itself introduces new \emph{internal errors}.
A procedure is called \emph{fault-tolerant} if it remains effective
even in the presence of these internal errors
\cite[Sec.~4]{gottesman_introduction_2009}.
To deal with internal errors that flip syndrome bits, multiple rounds
of syndrome measurements are performed.
One approach of implementing fault tolerance is using \acp{dem}.
A \ac{dem} abstracts away the underlying circuit,
focusing only on the relationship between possible errors
and their effects on the syndrome \cite[Sec.~1.4.3]{higgott_practical_2024}.
A \emph{detector error matrix} is generated from the circuit, which is
used for decoding instead of the original check matrix.
Decoding under a \ac{dem} poses a challenge with respect to the
latency constraint.
This is because the detector error matrix is much larger than the
check matrix of the underlying code, since it needs to represent many
more error locations.
For example, in our experiments using the $\llbracket 144,12,12
\rrbracket$ \ac{bb} code with $12$ syndrome measurement rounds, the
number of \acp{vn} grew from $144$ to $9504$ and the number of
\acp{cn} grew from $72$ to $1008$.
To keep the latency of \ac{dem} decoding manageable, one approach is
\emph{sliding-window decoding}.
Instead of decoding on the entire detector error matrix at once,
it is partitioned into several overlapping windows.
Once decoding of one window is complete, error estimates on the initial part
that is no longer needed are committed, and the next window is processed.
This way, decoding can start as soon as the syndrome bits required
for the first window have been extracted.
The idea originates with the \emph{overlapping recovery} scheme
proposed for the surface code in
\cite[Sec.~IV.B]{dennis_topological_2002} and has since been studied
for surface and toric codes \cite{kuo_fault-tolerant_2024} as well as
for \ac{qldpc} codes under both phenomenological and circuit-level
noise \cite{huang_increasing_2024,gong_toward_2024,kang_quits_2025}.
% Reseach gap + our work
We observe a structural similarity between sliding-window decoding for
\acp{dem} and window decoding for \ac{sc}-\acs{ldpc} codes.
In contrast to the latter, however, where \ac{bp} messages are
carried between windows \cite[Sec.~III.~C.]{hassan_fully_2016},
the existing realizations of sliding-window decoding for \ac{qec}
discard the soft information produced inside one window before moving
to the next.
We propose \emph{warm-start sliding-window decoding}, in which the
\ac{bp} messages from the overlap region of the previous window are
reused to initialize \ac{bp} in the current window in place of the
standard cold-start initialization.
We formulate the warm start first for plain \ac{bp} and then for
\ac{bpgd}, a variant of \ac{bp} with better convergence properties
for \ac{qec} codes.
The decoders are evaluated by Monte Carlo simulation on the
$\llbracket 144,12,12 \rrbracket$ \ac{bb} code under standard
circuit-based depolarizing noise over $12$ syndrome extraction rounds.
The main finding is that warm-starting yields a consistent
improvement at low iteration budgets, which is the regime relevant for
low-latency operation.
% Outline of the Thesis
\Cref{ch:Fundamentals} reviews the fundamentals of classical and
quantum error correction.
On the classical side, it covers binary linear block codes,
\ac{ldpc} and \ac{sc}-\ac{ldpc} codes, and the \ac{bp} decoding
algorithm.
On the quantum side, it introduces the relevant quantum mechanical
notation, stabilizer measurements, stabilizer codes, \acf{css} codes,
\ac{qldpc} codes, and the \ac{bpgd} algorithm.
\Cref{ch:Fault tolerance} introduces fault-tolerant \ac{qec}.
It formalizes the notion of fault tolerance, presents the noise
models considered in this work, and develops the \ac{dem} formalism
through the measurement syndrome matrix, the detector matrix, and the
detector error matrix.
The chapter closes with a discussion of practical considerations
including the choice of noise model, the per-round \acf{ler}, and the
Stim toolchain.
\Cref{ch:Decoding} considers practical aspects of decoding under \acp{dem}.
It reviews the existing literature on sliding-window decoding for
\ac{qec}, develops the formal windowing construction we build upon,
introduces the proposed warm-start sliding-window decoder for
plain \ac{bp} and for \ac{bpgd}, and reports numerical results on the
$\llbracket 144,12,12 \rrbracket$ \ac{bb} code.
% TODO: Possibly extend to mention specific proposed research directions
\Cref{ch:Conclusion} concludes the thesis and outlines directions for
further research.

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@@ -1 +1,129 @@
\chapter{Conclusion and Outlook} \chapter{Conclusion and Outlook}
\label{ch:Conclusion}
% Recap of motivation
This thesis investigated decoding under \acp{dem} for fault-tolerant
\ac{qec}, with a focus on low-latency decoding methods for \ac{qldpc} codes.
The repetition of the syndrome measurements, especially under
consideration of circuit-level noise, leads to a significant increase
in decoding complexity: in our experiments on the $\llbracket
144,12,12 \rrbracket$ \ac{bb} code with $12$ syndrome extraction
rounds, the check matrix grew from 144 \acp{vn} and 72
\acp{cn} to 9504 \acp{vn} and 1008 \acp{cn}.
% Recap of research gap and own work
Sliding-window decoding addresses the latency constraint by
exploiting the time-like locality of the syndrome extraction circuit,
which manifests as a block-diagonal structure in the detector error
matrix when detectors are defined as the difference of consecutive
syndrome measurement rounds.
We drew a comparison to windowed decoding for \ac{sc}-\ac{ldpc}
codes, but noted that the existing realizations of sliding-window
decoding discard the soft information produced inside one window
before moving to the next.
Building on this observation, we proposed warm-start sliding-window
decoding, in which the \ac{bp} messages on the edges crossing into
the overlap region of the previous window are reused to initialise
the corresponding messages of the next window in place of the
standard cold-start initialisation.
We formulated the warm start first for plain \ac{bp} and then for
\ac{bpgd}, the latter being attractive as an inner decoder because it
addresses the convergence problems caused by short cycles and
degeneracy in \ac{qldpc} Tanner graphs.
The decoders were evaluated by Monte Carlo simulation on the
$\llbracket 144,12,12 \rrbracket$ \ac{bb} code over $12$ syndrome
extraction rounds under standard circuit-based depolarizing noise.
We focused on a qualitative analysis, refraining from further
optimizations such as introducing a normalization parameter for the
min-sum algorithm.
% Recap of experimental conclusions
For plain min-sum \ac{bp}, the warm start was consistently beneficial
across the parameter ranges we examined. The size of the gain depended
on the overlap between consecutive windows: enlarging $W$ or
shrinking $F$, both of which enlarge the overlap, raised the
warm-start performance increase.
We argued that the underlying mechanism is an effective increase in
the number of \ac{bp} iterations spent on the \acp{vn} in the overlap
region: each such \ac{vn} is processed by multiple consecutive window
invocations, and the warm start lets these invocations accumulate
iterations on the same \acp{vn} rather than restarting from scratch.
The gain was most pronounced at low numbers of maximum iterations, where
every additional iteration carries proportionally more information.
For \ac{bpgd}, we noted that more information is available in the
overlap region of a window: in addition to the \ac{bp} messages,
there is information about which \acp{vn} were decimated and to what value.
Passing this decimation information to the next window in addition to
the messages turned out to worsen the performance considerably, which
we attributed to a premature hard decision of the \acp{vn} in the
overlap region.
Restricting the warm start to the \ac{bp} messages alone, removed this effect.
The resulting message-only warm start recovered a consistent
improvement over cold-start that followed the same qualitative
behaviour as for plain \ac{bp}: larger overlap, achieved by larger
$W$ or smaller $F$, yielded a larger gain, and the
performance difference was most pronounced at low numbers of maximum iterations.
% Implications from experimental results
These observations imply that the warm-start modification to
sliding-window decoding provides a consistent improvement, as long as
some care is taken with specifying the information to be passed to
the subsequent window.
Note that this comes at no additional cost to the decoding complexity,
since the only difference between warm- and cold-start sliding-window
decoding is the initialization of the \ac{bp} messages.
We expect similar behavior with other inner decoders that support
soft information initialization in the overlap region.
% Further research
Several directions for further research emerge from this work.
The most immediate is an extension of the evaluation to other
\ac{qldpc} code families, to other circuit-level noise models such as
SI1000 or EM3, and to a range of code sizes.
This would clarify the generality of the gain due to the warm-start
initialization.
We expect the qualitative findings to carry over, since the
underlying mechanism is structural rather than code-specific, but
quantifying the gain across code families and noise models is left to
future work.
A second direction is a systematic study of inner decoders under the
warm-start framework.
We considered plain min-sum \ac{bp} and \ac{bpgd}, but other
algorithms used for \ac{qldpc} decoding, such as automorphism
ensemble decoding \cite{koutsioumpas_automorphism_2025} or neural
\ac{bp} \cite{miao_quaternary_2025} may admit warm-start variants of their own.
A third direction is a softer treatment of the decimation state in \ac{bpgd}.
Rather than discarding the decimation information of the previous
window entirely, as in the message-only warm start used here, one
could encode the decimation decisions as strong but finite biases on
the channel \acp{llr} of the next window, allowing the new window's parity
checks to override them if the syndrome calls for it.
This would interpolate between the two warm-start variants studied here and
might combine the benefits of both.
A related question is whether the decimation schedule itself should
be aware of the window structure, for instance by deferring
decimation of \acp{vn} in the overlap region until they have been
visited by the next window.
A final direction is suggested by the structural similarity between
sliding-window decoding for \acp{dem} and windowed decoding for
\ac{sc}-\ac{ldpc} codes.
The current approach to generating the syndrome extraction circuitry
necessarily leads to a coupling width of one between adjacent
syndrome measurement rounds.
A natural question is whether the coupling width could be
increased, e.g., by interleaving two separate realizations of the
syndrome measurement circuitry instead of always repeating the same one.
Work in this direction would also be a step toward bringing
sliding-window decoding under DEMs within the scope of the analytical
machinery developed for SC-LDPC codes.

View File

@@ -0,0 +1,56 @@
\chapter*{Abstract}
% Current state of the art
\Ac{qec} protects fragile quantum states against decoherence by
encoding logical information into a larger number of physical qubits.
Because the syndrome extraction circuitry is itself implemented on
noisy quantum hardware, practical \ac{qec} must be fault-tolerant,
accounting for errors introduced by the correction procedure itself.
Fault tolerance considerations and the syndrome extraction circuit
are captured by \acp{dem}, which provide a unified framework for passing
this information to the decoder.
Accounting for fault tolerance substantially inflates the
decoding problem.
At the same time, \ac{qec} imposes strict latency constraints due to
the backlog problem, where syndrome data accumulates faster than it
can be decoded.
Together, these factors pose a serious challenge for practical decoders.
Sliding-window decoding addresses this challenge by exploiting the
repeated structure of the syndrome extraction circuitry, partitioning
the \ac{dem}'s check matrix into overlapping windows that can be
decoded sequentially.
This allows for an earlier start to the decoding process, before all
syndrome measurements have been completed, thereby lowering the latency.
% Our work: Identify research gap
In this thesis, we perform a review of the existing literature on
sliding-window decoding and draw an analogy to windowed
decoding for classical spatially-coupled low-density parity-check
(\acs{sc}-\acs{ldpc}) codes.
We recognize that in contrast to the latter, existing realizations
of sliding-window decoding for \ac{qec} discard the soft information
produced inside one window before moving to the next.
% Our work: Warm-start
% TODO: Quantify improvement. Also for conclusion
We propose warm-start sliding-window decoding, in which the
\ac{bp} messages on the edges crossing into the overlap region of the previous
window are reused to initialize the corresponding messages of the
next window.
The warm start is formulated first for plain \ac{bp} and then extended to
\ac{bp} with guided decimation (\acs{bpgd}).
For both plain min-sum \ac{bp} and \ac{bpgd} decoding, the warm-start
initialization provides a consistent improvement across all examined
parameter settings.
We attribute this to an effective increase in \ac{bp} iterations on
variable nodes in the overlap regions: each such VN is processed by
multiple consecutive windows, and warm-starting lets these
invocations accumulate iterations rather than restart from scratch.
Crucially, the warm-start modification incurs no additional
computational cost relative to cold-start decoding, as it differs
only in the initialization of the \ac{bp} messages.

111
src/thesis/clean_bibliography.sh Executable file
View File

@@ -0,0 +1,111 @@
sed -i "s/Świerkowska/{\\\\'S}wierkowska/" bibliography.bib
sed -i "s/Héctor/H{\\\\'e}ctor/" bibliography.bib
sed -i "s/Bombín/Bomb{\\\\'i}n/" bibliography.bib
sed -i "s/Zémor/Z{\\\\'e}mor/" bibliography.bib
sed -Ezi "s/\s(abstract|note|urldate|url|keywords|file) = \{[^}]*(\{[^}]*\}[^}]*)*\},?\n//g" bibliography.bib
# Normalize arXiv-only entries to @misc with howpublished = {arXiv:<id>}.
# Detection: doi matches 10.48550/arXiv.<id>. The IEEEtranSA .bst's @article
# handler needs a journal field (which preprints lack) and ignores publisher,
# so for arXiv preprints we coerce the type to @misc and add howpublished
# (the field the .bst actually prints for @misc).
python3 - <<'PY'
import re
path = "bibliography.bib"
with open(path) as f:
text = f.read()
doi_re = re.compile(r"doi\s*=\s*\{10\.48550/arXiv\.([^}]+)\}")
type_re = re.compile(r"^@([A-Za-z]+)\{", re.MULTILINE)
howpublished_re = re.compile(r"^\s*howpublished\s*=\s*\{", re.MULTILINE)
title_field_re = re.compile(r"\b(title|booktitle)\s*=\s*\{", re.IGNORECASE)
inner_brace_re = re.compile(r"\{([A-Za-z0-9]+)\}")
# Split into entries by scanning for top-level "@type{...}" blocks. We walk
# brace depth so that the closing "}" of the entry is matched correctly even
# if internal fields contain braces.
def split_entries(s):
out, i, n = [], 0, len(s)
while i < n:
m = type_re.search(s, i)
if not m:
out.append(("text", s[i:]))
break
if m.start() > i:
out.append(("text", s[i:m.start()]))
depth, j = 0, m.start()
while j < n:
c = s[j]
if c == "{":
depth += 1
elif c == "}":
depth -= 1
if depth == 0:
j += 1
break
j += 1
out.append(("entry", s[m.start():j]))
i = j
return out
def normalize_arxiv(entry):
doi_m = doi_re.search(entry)
if not doi_m:
return entry
arxiv_id = doi_m.group(1)
entry = type_re.sub("@misc{", entry, count=1)
if not howpublished_re.search(entry):
# insert howpublished as the last field, before the entry-closing "}"
entry = re.sub(
r"(,?)(\s*)\}\s*$",
lambda m: ("," if m.group(1) != "," else m.group(1))
+ m.group(2) + "\thowpublished = {arXiv:" + arxiv_id + "},\n}",
entry,
count=1,
)
return entry
# Strip protective braces around words inside title/booktitle values.
# BibTeX uses "{Word}" inside titles to preserve case against the bibliography
# style's title-casing rules. We keep that protection only when every character
# inside the braces is non-lowercase (e.g. acronyms like {NASA}); for ordinary
# words like {Quantum} we drop the braces so the style's casing applies.
def strip_title_braces(entry):
out, i, n = [], 0, len(entry)
while True:
m = title_field_re.search(entry, i)
if not m:
out.append(entry[i:])
break
out.append(entry[i:m.end()])
depth, j = 1, m.end()
while j < n and depth > 0:
c = entry[j]
if c == "{":
depth += 1
elif c == "}":
depth -= 1
if depth == 0:
break
j += 1
value = entry[m.end():j]
cleaned = inner_brace_re.sub(
lambda mm: mm.group(1) if any(c.islower() for c in mm.group(1)) else mm.group(0),
value,
)
out.append(cleaned)
if j < n:
out.append(entry[j])
i = j + 1
return "".join(out)
def transform(entry):
return strip_title_braces(normalize_arxiv(entry))
parts = split_entries(text)
new_text = "".join(transform(p) if kind == "entry" else p for kind, p in parts)
with open(path, "w") as f:
f.write(new_text)
PY

188
src/thesis/copy_sim_results.sh Executable file
View File

@@ -0,0 +1,188 @@
#!/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++%${#sp}:1}"
# done
# done
# Copy whole BP over max iter. results
echo -e "\rCopying whole over max_iter simulation results..."
echo -n ' '
for decoder in "SyndromeMinSumDecoder"; do
for p in 0.001 0.0025 0.004; do
SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bp,decoder.class_name=${decoder},simulation.phy_err_rate=${p}/"
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}/"
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

@@ -6,27 +6,44 @@
\usepackage{amsfonts} \usepackage{amsfonts}
\usepackage{mleftright} \usepackage{mleftright}
\usepackage{bm} \usepackage{bm}
\usepackage{bbm}
\usepackage{tikz} \usepackage{tikz}
\usepackage{xcolor} \usepackage{xcolor}
\usepackage{pgfplots} \usepackage{pgfplots}
\pgfplotsset{compat=newest} \pgfplotsset{compat=newest}
\usepackage{acro} \usepackage{acro}
\usepackage{braket} \usepackage{braket}
\usepackage{listings}
\usepackage{caption}
% \usepackage[ % \usepackage[
% backend=biber, % backend=biber,
% style=ieee, % style=ieee,
% sorting=nty, % sorting=nty,
% ]{biblatex} % ]{biblatex}
\usepackage{todonotes} % \usepackage{todonotes}
\usepackage{quantikz}
\usepackage{stmaryrd}
\usepackage{algorithm}
\usepackage[noEnd=false]{algpseudocodex}
\usepackage{nicematrix}
\usepackage{colortbl}
\usepackage{cleveref}
\usepackage{lipsum}
\usetikzlibrary{calc, positioning, arrows, fit} \usetikzlibrary{calc, positioning, arrows, fit}
\usetikzlibrary{external} \usetikzlibrary{external}
\tikzexternalize \tikzexternalize
\makeatletter % \makeatletter
\renewcommand{\todo}[2][]{\tikzexternaldisable\@todo[#1]{#2}\tikzexternalenable} % \renewcommand{\todo}[2][]{\tikzexternaldisable\@todo[#1]{#2}\tikzexternalenable}
\makeatother % \makeatother
\setcounter{MaxMatrixCols}{20}
\Crefname{equation}{}{}
\Crefname{section}{Section}{Sections}
\Crefname{subsection}{Subsection}{Subsections}
\Crefname{figure}{Figure}{Figures}
% %
% %
@@ -35,6 +52,8 @@
% %
\newcommand{\red}[1]{\textcolor{red}{#1}} \newcommand{\red}[1]{\textcolor{red}{#1}}
\newcommand{\content}[1]{\noindent\indent\red{[#1]\\}}
\newcommand{\figwidth}{10cm} \newcommand{\figwidth}{10cm}
\newcommand{\figheight}{7.5cm} \newcommand{\figheight}{7.5cm}
@@ -73,7 +92,9 @@
\thesisSupervisor{Jonathan Mandelbaum} \thesisSupervisor{Jonathan Mandelbaum}
\thesisStartDate{01.11.2025} \thesisStartDate{01.11.2025}
\thesisEndDate{04.05.2026} \thesisEndDate{04.05.2026}
\thesisSignatureDate{Signature date} \thesisSignatureDate{04.05.2026}
\thesisSignature{res/Unterschrift_AT_blue.png}
\thesisSignatureHeight{2.4cm}
\thesisLanguage{english} \thesisLanguage{english}
\begin{document} \begin{document}
@@ -82,7 +103,7 @@
\maketitle \maketitle
\newpage \newpage
% \include{chapters/abstract} \include{chapters/abstract}
\cleardoublepage \cleardoublepage
\pagenumbering{arabic} \pagenumbering{arabic}

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,12000,0.01675,0.0014066653566989773,201.0
0.0015,6000,0.048,0.004090796817048492,288.0
0.002,2000,0.124,0.010971798240880681,248.0
0.0025,2000,0.258,0.024560528611376475,516.0
0.003,2000,0.441,0.04731136584915907,882.0
0.0035,2000,0.6485,0.08344096230884013,1297.0
0.004,2000,0.8085,0.1286738833656923,1617.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 12000 0.01675 0.0014066653566989773 201.0
3 0.0015 6000 0.048 0.004090796817048492 288.0
4 0.002 2000 0.124 0.010971798240880681 248.0
5 0.0025 2000 0.258 0.024560528611376475 516.0
6 0.003 2000 0.441 0.04731136584915907 882.0
7 0.0035 2000 0.6485 0.08344096230884013 1297.0
8 0.004 2000 0.8085 0.1286738833656923 1617.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,50000,0.004,0.0003339460107422143,200.0
0.0015,14000,0.016,0.0013432122426282334,224.0
0.002,6000,0.0538333333333333,0.004600762670813663,322.99999999999983
0.0025,2000,0.1515,0.01359714508496701,303.0
0.003,2000,0.29,0.028137416075114108,580.0
0.0035,2000,0.485,0.05379783863208576,970.0
0.004,2000,0.657,0.08530878077130555,1314.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 50000 0.004 0.0003339460107422143 200.0
3 0.0015 14000 0.016 0.0013432122426282334 224.0
4 0.002 6000 0.0538333333333333 0.004600762670813663 322.99999999999983
5 0.0025 2000 0.1515 0.01359714508496701 303.0
6 0.003 2000 0.29 0.028137416075114108 580.0
7 0.0035 2000 0.485 0.05379783863208576 970.0
8 0.004 2000 0.657 0.08530878077130555 1314.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,74000,0.0027837837837837,0.000232278495492233,205.9999999999938
0.0015,20000,0.01065,0.0008918618165982828,213.0
0.002,6000,0.0386666666666666,0.003280778882142177,231.9999999999996
0.0025,2000,0.1005,0.008787514236290539,201.0
0.003,2000,0.2145,0.019918520513549032,429.0
0.0035,2000,0.3975,0.041343353576980935,795.0
0.004,2000,0.5975,0.07303396011007879,1195.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 74000 0.0027837837837837 0.000232278495492233 205.9999999999938
3 0.0015 20000 0.01065 0.0008918618165982828 213.0
4 0.002 6000 0.0386666666666666 0.003280778882142177 231.9999999999996
5 0.0025 2000 0.1005 0.008787514236290539 201.0
6 0.003 2000 0.2145 0.019918520513549032 429.0
7 0.0035 2000 0.3975 0.041343353576980935 795.0
8 0.004 2000 0.5975 0.07303396011007879 1195.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.05975,0.005120966383739489,239.0
0.0015,2000,0.12,0.010596241035318976,240.0
0.002,2000,0.2925,0.02842304828215303,585.0
0.0025,2000,0.457,0.049614097064849094,914.0
0.003,2000,0.6565,0.08519774084658893,1313.0
0.0035,2000,0.807,0.12810716433630664,1614.0
0.004,2000,0.927,0.19596138832598886,1854.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.05975 0.005120966383739489 239.0
3 0.0015 2000 0.12 0.010596241035318976 240.0
4 0.002 2000 0.2925 0.02842304828215303 585.0
5 0.0025 2000 0.457 0.049614097064849094 914.0
6 0.003 2000 0.6565 0.08519774084658893 1313.0
7 0.0035 2000 0.807 0.12810716433630664 1614.0
8 0.004 2000 0.927 0.19596138832598886 1854.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,30000,0.0074,0.0006187681363896136,222.0
0.0015,8000,0.027375,0.002310383366790014,219.0
0.002,4000,0.081,0.007014379974311313,324.0
0.0025,2000,0.1935,0.01776132322220747,387.0
0.003,2000,0.3505,0.03532372820929974,701.0
0.0035,2000,0.549,0.06420358199217457,1098.0
0.004,2000,0.736,0.10504679589131227,1472.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 30000 0.0074 0.0006187681363896136 222.0
3 0.0015 8000 0.027375 0.002310383366790014 219.0
4 0.002 4000 0.081 0.007014379974311313 324.0
5 0.0025 2000 0.1935 0.01776132322220747 387.0
6 0.003 2000 0.3505 0.03532372820929974 701.0
7 0.0035 2000 0.549 0.06420358199217457 1098.0
8 0.004 2000 0.736 0.10504679589131227 1472.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,56000,0.0035892857142857,0.0002996003321397156,200.99999999999918
0.0015,16000,0.0141875,0.001190050056010028,227.0
0.002,6000,0.0458333333333333,0.003902110220303623,274.99999999999983
0.0025,2000,0.127,0.011254499159800035,254.0
0.003,2000,0.255,0.024232483954962025,510.0
0.0035,2000,0.455,0.049322879977013234,910.0
0.004,2000,0.629,0.07930773938046853,1258.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 56000 0.0035892857142857 0.0002996003321397156 200.99999999999918
3 0.0015 16000 0.0141875 0.001190050056010028 227.0
4 0.002 6000 0.0458333333333333 0.003902110220303623 274.99999999999983
5 0.0025 2000 0.127 0.011254499159800035 254.0
6 0.003 2000 0.255 0.024232483954962025 510.0
7 0.0035 2000 0.455 0.049322879977013234 910.0
8 0.004 2000 0.629 0.07930773938046853 1258.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.632,0.07993046327730713,1264.0
0.0015,2000,0.7685,0.11479080536457342,1537.0
0.002,2000,0.8905,0.16832973055592892,1781.0
0.0025,2000,0.9405,0.2095463416012857,1881.0
0.003,2000,0.9765,0.26843039175484296,1953.0
0.0035,2000,0.993,0.33865993052589327,1986.0
0.004,2000,0.995,0.3569459165824279,1990.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.632 0.07993046327730713 1264.0
3 0.0015 2000 0.7685 0.11479080536457342 1537.0
4 0.002 2000 0.8905 0.16832973055592892 1781.0
5 0.0025 2000 0.9405 0.2095463416012857 1881.0
6 0.003 2000 0.9765 0.26843039175484296 1953.0
7 0.0035 2000 0.993 0.33865993052589327 1986.0
8 0.004 2000 0.995 0.3569459165824279 1990.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0361666666666666,0.003065034000747535,216.99999999999957
0.0015,4000,0.08675,0.007533613442062825,347.0
0.002,2000,0.183,0.01670196477645869,366.0
0.0025,2000,0.3605,0.036570265848455796,721.0
0.003,2000,0.5385,0.062407102537387016,1077.0
0.0035,2000,0.7385,0.10575612450061989,1477.0
0.004,2000,0.8635,0.15291357705621333,1727.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0361666666666666 0.003065034000747535 216.99999999999957
3 0.0015 4000 0.08675 0.007533613442062825 347.0
4 0.002 2000 0.183 0.01670196477645869 366.0
5 0.0025 2000 0.3605 0.036570265848455796 721.0
6 0.003 2000 0.5385 0.062407102537387016 1077.0
7 0.0035 2000 0.7385 0.10575612450061989 1477.0
8 0.004 2000 0.8635 0.15291357705621333 1727.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,32000,0.0065,0.0005432871152698526,208.0
0.0015,10000,0.0211,0.0017755706988360487,211.0
0.002,4000,0.067,0.005762505879780444,268.0
0.0025,2000,0.1555,0.013985493383097625,311.0
0.003,2000,0.2855,0.02762559348483462,571.0
0.0035,2000,0.4885,0.05433539011619826,977.0
0.004,2000,0.678,0.09011189125403751,1356.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 32000 0.0065 0.0005432871152698526 208.0
3 0.0015 10000 0.0211 0.0017755706988360487 211.0
4 0.002 4000 0.067 0.005762505879780444 268.0
5 0.0025 2000 0.1555 0.013985493383097625 311.0
6 0.003 2000 0.2855 0.02762559348483462 571.0
7 0.0035 2000 0.4885 0.05433539011619826 977.0
8 0.004 2000 0.678 0.09011189125403751 1356.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,16000,0.01375,0.0011531185491073792,220.0
0.0015,6000,0.0416666666666666,0.0035403526553423603,249.9999999999996
0.002,2000,0.11,0.009664150391878956,220.0
0.0025,2000,0.2535,0.024068915462335805,507.0
0.003,2000,0.4185,0.04417333224775788,837.0
0.0035,2000,0.62,0.0774668808446417,1240.0
0.004,2000,0.792,0.12265189055421477,1584.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 16000 0.01375 0.0011531185491073792 220.0
3 0.0015 6000 0.0416666666666666 0.0035403526553423603 249.9999999999996
4 0.002 2000 0.11 0.009664150391878956 220.0
5 0.0025 2000 0.2535 0.024068915462335805 507.0
6 0.003 2000 0.4185 0.04417333224775788 837.0
7 0.0035 2000 0.62 0.0774668808446417 1240.0
8 0.004 2000 0.792 0.12265189055421477 1584.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,62000,0.0032903225806451,0.0002746079212814223,203.99999999999622
0.0015,16000,0.0134375,0.0011267480946226538,215.0
0.002,6000,0.0453333333333333,0.0038586229394146354,271.99999999999983
0.0025,2000,0.1265,0.011207320558933254,253.0
0.003,2000,0.252,0.02390564797425576,504.0
0.0035,2000,0.453,0.04903264087587211,906.0
0.004,2000,0.6265,0.07879231884746019,1253.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 62000 0.0032903225806451 0.0002746079212814223 203.99999999999622
3 0.0015 16000 0.0134375 0.0011267480946226538 215.0
4 0.002 6000 0.0453333333333333 0.0038586229394146354 271.99999999999983
5 0.0025 2000 0.1265 0.011207320558933254 253.0
6 0.003 2000 0.252 0.02390564797425576 504.0
7 0.0035 2000 0.453 0.04903264087587211 906.0
8 0.004 2000 0.6265 0.07879231884746019 1253.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00162,0.00013510034136854365,162.0
0.0015,26000,0.0079615384615384,0.00066589492156377,206.9999999999984
0.002,8000,0.027,0.0022783337152086913,216.0
0.0025,4000,0.0855,0.0074204821894011674,342.0
0.003,2000,0.1795,0.016351617556473186,359.0
0.0035,2000,0.345,0.034645612003118,690.0
0.004,2000,0.5415,0.06291652725715624,1083.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.00162 0.00013510034136854365 162.0
3 0.0015 26000 0.0079615384615384 0.00066589492156377 206.9999999999984
4 0.002 8000 0.027 0.0022783337152086913 216.0
5 0.0025 4000 0.0855 0.0074204821894011674 342.0
6 0.003 2000 0.1795 0.016351617556473186 359.0
7 0.0035 2000 0.345 0.034645612003118 690.0
8 0.004 2000 0.5415 0.06291652725715624 1083.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.057,0.004878809452940613,228.0
0.0015,2000,0.1345,0.011965166585961362,269.0
0.002,2000,0.2835,0.02739906464725228,567.0
0.0025,2000,0.4645,0.050714990274915994,929.0
0.003,2000,0.649,0.08354968174320077,1298.0
0.0035,2000,0.799,0.125151191269673,1598.0
0.004,2000,0.923,0.19237907929568254,1846.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.057 0.004878809452940613 228.0
3 0.0015 2000 0.1345 0.011965166585961362 269.0
4 0.002 2000 0.2835 0.02739906464725228 567.0
5 0.0025 2000 0.4645 0.050714990274915994 929.0
6 0.003 2000 0.649 0.08354968174320077 1298.0
7 0.0035 2000 0.799 0.125151191269673 1598.0
8 0.004 2000 0.923 0.19237907929568254 1846.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,28000,0.0072857142857142,0.0006091797682086231,203.9999999999976
0.0015,8000,0.026875,0.0022676530141574336,215.0
0.002,4000,0.07125,0.006140708552619056,285.0
0.0025,2000,0.181,0.016501598292156028,362.0
0.003,2000,0.343,0.0344003178522726,686.0
0.0035,2000,0.539,0.06249179545899253,1078.0
0.004,2000,0.734,0.10448375252924946,1468.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 28000 0.0072857142857142 0.0006091797682086231 203.9999999999976
3 0.0015 8000 0.026875 0.0022676530141574336 215.0
4 0.002 4000 0.07125 0.006140708552619056 285.0
5 0.0025 2000 0.181 0.016501598292156028 362.0
6 0.003 2000 0.343 0.0344003178522726 686.0
7 0.0035 2000 0.539 0.06249179545899253 1078.0
8 0.004 2000 0.734 0.10448375252924946 1468.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,66000,0.0031060606060606,0.0002592076019299894,204.9999999999996
0.0015,16000,0.0130625,0.0010951136545078732,209.0
0.002,6000,0.0398333333333333,0.003381635886214096,238.99999999999977
0.0025,2000,0.108,0.009478884979367552,216.0
0.003,2000,0.241,0.022717441549556572,482.0
0.0035,2000,0.427,0.04534551221126004,854.0
0.004,2000,0.616,0.07666151943586219,1232.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 66000 0.0031060606060606 0.0002592076019299894 204.9999999999996
3 0.0015 16000 0.0130625 0.0010951136545078732 209.0
4 0.002 6000 0.0398333333333333 0.003381635886214096 238.99999999999977
5 0.0025 2000 0.108 0.009478884979367552 216.0
6 0.003 2000 0.241 0.022717441549556572 482.0
7 0.0035 2000 0.427 0.04534551221126004 854.0
8 0.004 2000 0.616 0.07666151943586219 1232.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.632,0.07993046327730713,1264.0
0.0015,2000,0.7685,0.11479080536457342,1537.0
0.002,2000,0.8905,0.16832973055592892,1781.0
0.0025,2000,0.9405,0.2095463416012857,1881.0
0.003,2000,0.9765,0.26843039175484296,1953.0
0.0035,2000,0.993,0.33865993052589327,1986.0
0.004,2000,0.995,0.3569459165824279,1990.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.632 0.07993046327730713 1264.0
3 0.0015 2000 0.7685 0.11479080536457342 1537.0
4 0.002 2000 0.8905 0.16832973055592892 1781.0
5 0.0025 2000 0.9405 0.2095463416012857 1881.0
6 0.003 2000 0.9765 0.26843039175484296 1953.0
7 0.0035 2000 0.993 0.33865993052589327 1986.0
8 0.004 2000 0.995 0.3569459165824279 1990.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0343333333333333,0.0029071468641445053,205.9999999999998
0.0015,4000,0.09775,0.008535335041573222,391.0
0.002,2000,0.2005,0.018474608554528427,401.0
0.0025,2000,0.347,0.03489159369123396,694.0
0.003,2000,0.559,0.06595052116772404,1118.0
0.0035,2000,0.735,0.1047647873005133,1470.0
0.004,2000,0.867,0.15474521742325598,1734.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0343333333333333 0.0029071468641445053 205.9999999999998
3 0.0015 4000 0.09775 0.008535335041573222 391.0
4 0.002 2000 0.2005 0.018474608554528427 401.0
5 0.0025 2000 0.347 0.03489159369123396 694.0
6 0.003 2000 0.559 0.06595052116772404 1118.0
7 0.0035 2000 0.735 0.1047647873005133 1470.0
8 0.004 2000 0.867 0.15474521742325598 1734.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,34000,0.0061176470588235,0.0005112389838239917,207.999999999999
0.0015,12000,0.0199166666666666,0.0016750685805796417,238.9999999999992
0.002,4000,0.05925,0.005076889602981138,237.0
0.0025,2000,0.1465,0.013114062821618089,293.0
0.003,2000,0.297,0.028939525764745788,594.0
0.0035,2000,0.4765,0.05250617012872005,953.0
0.004,2000,0.664,0.08687912132657749,1328.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 34000 0.0061176470588235 0.0005112389838239917 207.999999999999
3 0.0015 12000 0.0199166666666666 0.0016750685805796417 238.9999999999992
4 0.002 4000 0.05925 0.005076889602981138 237.0
5 0.0025 2000 0.1465 0.013114062821618089 293.0
6 0.003 2000 0.297 0.028939525764745788 594.0
7 0.0035 2000 0.4765 0.05250617012872005 953.0
8 0.004 2000 0.664 0.08687912132657749 1328.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.08375,0.0072623363421430165,335.0
0.0015,2000,0.17,0.015407535303274322,340.0
0.002,2000,0.333,0.03318402118027908,666.0
0.0025,2000,0.5225,0.05974038898813494,1045.0
0.003,2000,0.7125,0.09866447739264284,1425.0
0.0035,2000,0.8475,0.14505307692276814,1695.0
0.004,2000,0.936,0.20472927123294937,1872.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.08375 0.0072623363421430165 335.0
3 0.0015 2000 0.17 0.015407535303274322 340.0
4 0.002 2000 0.333 0.03318402118027908 666.0
5 0.0025 2000 0.5225 0.05974038898813494 1045.0
6 0.003 2000 0.7125 0.09866447739264284 1425.0
7 0.0035 2000 0.8475 0.14505307692276814 1695.0
8 0.004 2000 0.936 0.20472927123294937 1872.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.05375,0.00459345717599724,215.0
0.0015,2000,0.137,0.012203310556051061,274.0
0.002,2000,0.248,0.023471730814805247,496.0
0.0025,2000,0.424,0.044929992453897394,848.0
0.003,2000,0.6005,0.07361169169753423,1201.0
0.0035,2000,0.7845,0.12005821823758633,1569.0
0.004,2000,0.9005,0.1749405238157723,1801.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.05375 0.00459345717599724 215.0
3 0.0015 2000 0.137 0.012203310556051061 274.0
4 0.002 2000 0.248 0.023471730814805247 496.0
5 0.0025 2000 0.424 0.044929992453897394 848.0
6 0.003 2000 0.6005 0.07361169169753423 1201.0
7 0.0035 2000 0.7845 0.12005821823758633 1569.0
8 0.004 2000 0.9005 0.1749405238157723 1801.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0555,0.004746996564855888,222.0
0.0015,2000,0.122,0.010783823589648356,244.0
0.002,2000,0.228,0.02133338177466315,456.0
0.0025,2000,0.3975,0.041343353576980935,795.0
0.003,2000,0.577,0.06918859214518802,1154.0
0.0035,2000,0.7605,0.11228111333332969,1521.0
0.004,2000,0.8835,0.16402396604923497,1767.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0555 0.004746996564855888 222.0
3 0.0015 2000 0.122 0.010783823589648356 244.0
4 0.002 2000 0.228 0.02133338177466315 456.0
5 0.0025 2000 0.3975 0.041343353576980935 795.0
6 0.003 2000 0.577 0.06918859214518802 1154.0
7 0.0035 2000 0.7605 0.11228111333332969 1521.0
8 0.004 2000 0.8835 0.16402396604923497 1767.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.1275,0.011301702536387737,255.0
0.0015,2000,0.2445,0.023093785381261167,489.0
0.002,2000,0.471,0.05168059078836085,942.0
0.0025,2000,0.6925,0.09359889423026135,1385.0
0.003,2000,0.83,0.13727825732341103,1660.0
0.0035,2000,0.927,0.19596138832598886,1854.0
0.004,2000,0.9745,0.2634339765587691,1949.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.1275 0.011301702536387737 255.0
3 0.0015 2000 0.2445 0.023093785381261167 489.0
4 0.002 2000 0.471 0.05168059078836085 942.0
5 0.0025 2000 0.6925 0.09359889423026135 1385.0
6 0.003 2000 0.83 0.13727825732341103 1660.0
7 0.0035 2000 0.927 0.19596138832598886 1854.0
8 0.004 2000 0.9745 0.2634339765587691 1949.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.05525,0.004725046408614819,221.0
0.0015,2000,0.133,0.011822582694107964,266.0
0.002,2000,0.2755,0.026498707449347236,551.0
0.0025,2000,0.462,0.050346464045528894,924.0
0.003,2000,0.641,0.08182695829978004,1282.0
0.0035,2000,0.8035,0.12680036354194668,1607.0
0.004,2000,0.9095,0.18143334698302127,1819.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.05525 0.004725046408614819 221.0
3 0.0015 2000 0.133 0.011822582694107964 266.0
4 0.002 2000 0.2755 0.026498707449347236 551.0
5 0.0025 2000 0.462 0.050346464045528894 924.0
6 0.003 2000 0.641 0.08182695829978004 1282.0
7 0.0035 2000 0.8035 0.12680036354194668 1607.0
8 0.004 2000 0.9095 0.18143334698302127 1819.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.048,0.004090796817048492,288.0
0.0015,2000,0.115,0.010128988904076097,230.0
0.002,2000,0.2155,0.02002255762528382,431.0
0.0025,2000,0.402,0.04194208019539358,804.0
0.003,2000,0.577,0.06918859214518802,1154.0
0.0035,2000,0.764,0.11336949998487811,1528.0
0.004,2000,0.897,0.17256014533992214,1794.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.048 0.004090796817048492 288.0
3 0.0015 2000 0.115 0.010128988904076097 230.0
4 0.002 2000 0.2155 0.02002255762528382 431.0
5 0.0025 2000 0.402 0.04194208019539358 804.0
6 0.003 2000 0.577 0.06918859214518802 1154.0
7 0.0035 2000 0.764 0.11336949998487811 1528.0
8 0.004 2000 0.897 0.17256014533992214 1794.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.6955,0.09433912151694923,1391.0
0.0015,2000,0.816,0.13156999840650407,1632.0
0.002,2000,0.9215,0.19107956872744314,1843.0
0.0025,2000,0.9595,0.2344834483240309,1919.0
0.003,2000,0.9895,0.31593226271987895,1979.0
0.0035,2000,0.997,0.3837454986270925,1994.0
0.004,2000,0.999,0.4376586748096508,1998.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.6955 0.09433912151694923 1391.0
3 0.0015 2000 0.816 0.13156999840650407 1632.0
4 0.002 2000 0.9215 0.19107956872744314 1843.0
5 0.0025 2000 0.9595 0.2344834483240309 1919.0
6 0.003 2000 0.9895 0.31593226271987895 1979.0
7 0.0035 2000 0.997 0.3837454986270925 1994.0
8 0.004 2000 0.999 0.4376586748096508 1998.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.09425,0.0082153967557419,377.0
0.0015,2000,0.206,0.019039074473767514,412.0
0.002,2000,0.371,0.03789851025936897,742.0
0.0025,2000,0.5865,0.07094884804525436,1173.0
0.003,2000,0.7685,0.11479080536457342,1537.0
0.0035,2000,0.8965,0.17222616291377513,1793.0
0.004,2000,0.9575,0.2314023053376273,1915.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.09425 0.0082153967557419 377.0
3 0.0015 2000 0.206 0.019039074473767514 412.0
4 0.002 2000 0.371 0.03789851025936897 742.0
5 0.0025 2000 0.5865 0.07094884804525436 1173.0
6 0.003 2000 0.7685 0.11479080536457342 1537.0
7 0.0035 2000 0.8965 0.17222616291377513 1793.0
8 0.004 2000 0.9575 0.2314023053376273 1915.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0488333333333333,0.004163473418041463,292.9999999999998
0.0015,2000,0.1225,0.01083078042647323,245.0
0.002,2000,0.2435,0.022986095764761516,487.0
0.0025,2000,0.4055,0.042410618607193085,811.0
0.003,2000,0.5965,0.07284225986971693,1193.0
0.0035,2000,0.7945,0.12353552306518623,1589.0
0.004,2000,0.9,0.1745958147319816,1800.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0488333333333333 0.004163473418041463 292.9999999999998
3 0.0015 2000 0.1225 0.01083078042647323 245.0
4 0.002 2000 0.2435 0.022986095764761516 487.0
5 0.0025 2000 0.4055 0.042410618607193085 811.0
6 0.003 2000 0.5965 0.07284225986971693 1193.0
7 0.0035 2000 0.7945 0.12353552306518623 1589.0
8 0.004 2000 0.9 0.1745958147319816 1800.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.033,0.0027924923467828044,264.0
0.0015,4000,0.0885,0.0076922358935922475,354.0
0.002,2000,0.189,0.01730577346851303,378.0
0.0025,2000,0.386,0.039831698576282215,772.0
0.003,2000,0.5745,0.06873139184884758,1149.0
0.0035,2000,0.7675,0.11447278468704636,1535.0
0.004,2000,0.8925,0.16960631326972486,1785.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.033 0.0027924923467828044 264.0
3 0.0015 4000 0.0885 0.0076922358935922475 354.0
4 0.002 2000 0.189 0.01730577346851303 378.0
5 0.0025 2000 0.386 0.039831698576282215 772.0
6 0.003 2000 0.5745 0.06873139184884758 1149.0
7 0.0035 2000 0.7675 0.11447278468704636 1535.0
8 0.004 2000 0.8925 0.16960631326972486 1785.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,16000,0.013375,0.0011214749225721965,214.0
0.0015,6000,0.0436666666666666,0.0037138159693325123,261.9999999999996
0.002,2000,0.1125,0.009896269575755956,225.0
0.0025,2000,0.2375,0.022342685193895928,475.0
0.003,2000,0.4105,0.04308436449639608,821.0
0.0035,2000,0.621,0.07766943516436708,1242.0
0.004,2000,0.799,0.125151191269673,1598.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 16000 0.013375 0.0011214749225721965 214.0
3 0.0015 6000 0.0436666666666666 0.0037138159693325123 261.9999999999996
4 0.002 2000 0.1125 0.009896269575755956 225.0
5 0.0025 2000 0.2375 0.022342685193895928 475.0
6 0.003 2000 0.4105 0.04308436449639608 821.0
7 0.0035 2000 0.621 0.07766943516436708 1242.0
8 0.004 2000 0.799 0.125151191269673 1598.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,20000,0.01,0.0008371773591205889,200.0
0.0015,8000,0.02975,0.002513627927773654,238.0
0.002,4000,0.08025,0.0069468735550100025,321.0
0.0025,2000,0.2055,0.018987611527110704,411.0
0.003,2000,0.3465,0.03483003359216841,693.0
0.0035,2000,0.556,0.06542265847616091,1112.0
0.004,2000,0.738,0.1056137629395989,1476.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 20000 0.01 0.0008371773591205889 200.0
3 0.0015 8000 0.02975 0.002513627927773654 238.0
4 0.002 4000 0.08025 0.0069468735550100025 321.0
5 0.0025 2000 0.2055 0.018987611527110704 411.0
6 0.003 2000 0.3465 0.03483003359216841 693.0
7 0.0035 2000 0.556 0.06542265847616091 1112.0
8 0.004 2000 0.738 0.1056137629395989 1476.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.102,0.008925364554660087,408.0
0.0015,2000,0.234,0.02196950237720341,468.0
0.002,2000,0.433,0.04618256897389805,866.0
0.0025,2000,0.6455,0.08279160735454238,1291.0
0.003,2000,0.82,0.13315913781420163,1640.0
0.0035,2000,0.922,0.19151019058730434,1844.0
0.004,2000,0.9805,0.2797174651647023,1961.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.102 0.008925364554660087 408.0
3 0.0015 2000 0.234 0.02196950237720341 468.0
4 0.002 2000 0.433 0.04618256897389805 866.0
5 0.0025 2000 0.6455 0.08279160735454238 1291.0
6 0.003 2000 0.82 0.13315913781420163 1640.0
7 0.0035 2000 0.922 0.19151019058730434 1844.0
8 0.004 2000 0.9805 0.2797174651647023 1961.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0355,0.0030075886692517706,212.99999999999997
0.0015,4000,0.0835,0.007239766684647431,334.0
0.002,2000,0.2025,0.018679455867679495,405.0
0.0025,2000,0.3635,0.036947712076332184,727.0
0.003,2000,0.5605,0.06621568805942701,1121.0
0.0035,2000,0.749,0.10880485867108969,1498.0
0.004,2000,0.8895,0.1676994342621433,1779.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0355 0.0030075886692517706 212.99999999999997
3 0.0015 4000 0.0835 0.007239766684647431 334.0
4 0.002 2000 0.2025 0.018679455867679495 405.0
5 0.0025 2000 0.3635 0.036947712076332184 727.0
6 0.003 2000 0.5605 0.06621568805942701 1121.0
7 0.0035 2000 0.749 0.10880485867108969 1498.0
8 0.004 2000 0.8895 0.1676994342621433 1779.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,12000,0.0174166666666666,0.0014631053822830031,208.9999999999992
0.0015,4000,0.051,0.004352706093600722,204.0
0.002,2000,0.1315,0.011680224751058454,263.0
0.0025,2000,0.281,0.02711671729858034,562.0
0.003,2000,0.46,0.050052771570453625,920.0
0.0035,2000,0.662,0.08642741539493726,1324.0
0.004,2000,0.8145,0.13098222531638515,1629.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 12000 0.0174166666666666 0.0014631053822830031 208.9999999999992
3 0.0015 4000 0.051 0.004352706093600722 204.0
4 0.002 2000 0.1315 0.011680224751058454 263.0
5 0.0025 2000 0.281 0.02711671729858034 562.0
6 0.003 2000 0.46 0.050052771570453625 920.0
7 0.0035 2000 0.662 0.08642741539493726 1324.0
8 0.004 2000 0.8145 0.13098222531638515 1629.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.6955,0.09433912151694923,1391.0
0.0015,2000,0.816,0.13156999840650407,1632.0
0.002,2000,0.9215,0.19107956872744314,1843.0
0.0025,2000,0.9595,0.2344834483240309,1919.0
0.003,2000,0.9895,0.31593226271987895,1979.0
0.0035,2000,0.997,0.3837454986270925,1994.0
0.004,2000,0.999,0.4376586748096508,1998.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.6955 0.09433912151694923 1391.0
3 0.0015 2000 0.816 0.13156999840650407 1632.0
4 0.002 2000 0.9215 0.19107956872744314 1843.0
5 0.0025 2000 0.9595 0.2344834483240309 1919.0
6 0.003 2000 0.9895 0.31593226271987895 1979.0
7 0.0035 2000 0.997 0.3837454986270925 1994.0
8 0.004 2000 0.999 0.4376586748096508 1998.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.087,0.0075562567245422985,348.0
0.0015,2000,0.2025,0.018679455867679495,405.0
0.002,2000,0.3515,0.035447587291447924,703.0
0.0025,2000,0.5605,0.06621568805942701,1121.0
0.003,2000,0.766,0.11399809680348838,1532.0
0.0035,2000,0.896,0.1718936562142762,1792.0
0.004,2000,0.9535,0.22561949205779908,1907.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.087 0.0075562567245422985 348.0
3 0.0015 2000 0.2025 0.018679455867679495 405.0
4 0.002 2000 0.3515 0.035447587291447924 703.0
5 0.0025 2000 0.5605 0.06621568805942701 1121.0
6 0.003 2000 0.766 0.11399809680348838 1532.0
7 0.0035 2000 0.896 0.1718936562142762 1792.0
8 0.004 2000 0.9535 0.22561949205779908 1907.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0341666666666666,0.0028928071163165647,204.9999999999996
0.0015,4000,0.0915,0.007964810720254789,366.0
0.002,2000,0.202,0.018628199928893086,404.0
0.0025,2000,0.3685,0.03758042822058505,737.0
0.003,2000,0.562,0.06648168584179992,1124.0
0.0035,2000,0.7435,0.10719362881586803,1487.0
0.004,2000,0.876,0.15966624844871136,1752.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0341666666666666 0.0028928071163165647 204.9999999999996
3 0.0015 4000 0.0915 0.007964810720254789 366.0
4 0.002 2000 0.202 0.018628199928893086 404.0
5 0.0025 2000 0.3685 0.03758042822058505 737.0
6 0.003 2000 0.562 0.06648168584179992 1124.0
7 0.0035 2000 0.7435 0.10719362881586803 1487.0
8 0.004 2000 0.876 0.15966624844871136 1752.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,24000,0.008875,0.0007426089118820478,212.99999999999997
0.0015,8000,0.027375,0.002310383366790014,219.0
0.002,4000,0.0805,0.006969370086301163,322.0
0.0025,2000,0.1765,0.016052408593168255,353.0
0.003,2000,0.321,0.03174633874742727,642.0
0.0035,2000,0.5295,0.06089683913260491,1059.0
0.004,2000,0.703,0.09621935287123151,1406.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 24000 0.008875 0.0007426089118820478 212.99999999999997
3 0.0015 8000 0.027375 0.002310383366790014 219.0
4 0.002 4000 0.0805 0.006969370086301163 322.0
5 0.0025 2000 0.1765 0.016052408593168255 353.0
6 0.003 2000 0.321 0.03174633874742727 642.0
7 0.0035 2000 0.5295 0.06089683913260491 1059.0
8 0.004 2000 0.703 0.09621935287123151 1406.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.0018,0.00015012389249957625,180.0
0.0015,30000,0.0071666666666666,0.000599192960614614,214.999999999998
0.002,8000,0.026125,0.0022035952056765895,209.0
0.0025,4000,0.08375,0.0072623363421430165,335.0
0.003,2000,0.184,0.016802316683105167,368.0
0.0035,2000,0.344,0.0345228792367418,688.0
0.004,2000,0.5175,0.058923829667395955,1035.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.0018 0.00015012389249957625 180.0
3 0.0015 30000 0.0071666666666666 0.000599192960614614 214.999999999998
4 0.002 8000 0.026125 0.0022035952056765895 209.0
5 0.0025 4000 0.08375 0.0072623363421430165 335.0
6 0.003 2000 0.184 0.016802316683105167 368.0
7 0.0035 2000 0.344 0.0345228792367418 688.0
8 0.004 2000 0.5175 0.058923829667395955 1035.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.0007,5.83520569852336e-05,70.0
0.0015,62000,0.003258064516129,0.0002719116557121648,201.999999999998
0.002,16000,0.013,0.0010898423190723872,208.0
0.0025,6000,0.0468333333333333,0.0039891474854014675,280.99999999999983
0.003,2000,0.1165,0.010268909922777514,233.0
0.0035,2000,0.2525,0.023960037096822373,505.0
0.004,2000,0.4255,0.04513750370753944,851.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.0007 5.83520569852336e-05 70.0
3 0.0015 62000 0.003258064516129 0.0002719116557121648 201.999999999998
4 0.002 16000 0.013 0.0010898423190723872 208.0
5 0.0025 6000 0.0468333333333333 0.0039891474854014675 280.99999999999983
6 0.003 2000 0.1165 0.010268909922777514 233.0
7 0.0035 2000 0.2525 0.023960037096822373 505.0
8 0.004 2000 0.4255 0.04513750370753944 851.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0453333333333333,0.0038586229394146354,271.99999999999983
0.0015,4000,0.09175,0.007987562516493574,367.0
0.002,2000,0.199,0.018321281103642173,398.0
0.0025,2000,0.362,0.036758785596775034,724.0
0.003,2000,0.5155,0.058599376123828484,1031.0
0.0035,2000,0.7085,0.09762605599754803,1417.0
0.004,2000,0.856,0.149129354542893,1712.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0453333333333333 0.0038586229394146354 271.99999999999983
3 0.0015 4000 0.09175 0.007987562516493574 367.0
4 0.002 2000 0.199 0.018321281103642173 398.0
5 0.0025 2000 0.362 0.036758785596775034 724.0
6 0.003 2000 0.5155 0.058599376123828484 1031.0
7 0.0035 2000 0.7085 0.09762605599754803 1417.0
8 0.004 2000 0.856 0.149129354542893 1712.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,44000,0.0045454545454545,0.00037957931952325996,199.999999999998
0.0015,16000,0.014625,0.001226996590199092,234.0
0.002,6000,0.046,0.003916610622698213,276.0
0.0025,2000,0.128,0.011348930715916694,256.0
0.003,2000,0.239,0.022503101573992157,478.0
0.0035,2000,0.4195,0.044310417497246735,839.0
0.004,2000,0.5965,0.07284225986971693,1193.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 44000 0.0045454545454545 0.00037957931952325996 199.999999999998
3 0.0015 16000 0.014625 0.001226996590199092 234.0
4 0.002 6000 0.046 0.003916610622698213 276.0
5 0.0025 2000 0.128 0.011348930715916694 256.0
6 0.003 2000 0.239 0.022503101573992157 478.0
7 0.0035 2000 0.4195 0.044310417497246735 839.0
8 0.004 2000 0.5965 0.07284225986971693 1193.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00122,0.00010172355962756452,122.0
0.0015,42000,0.0047857142857142,0.0003996869775206857,200.9999999999964
0.002,12000,0.0196666666666666,0.001653849971735899,235.9999999999992
0.0025,4000,0.066,0.0056737465539274945,264.0
0.003,2000,0.1485,0.01330698362831062,297.0
0.0035,2000,0.3085,0.03027331056488236,617.0
0.004,2000,0.473,0.05197988715416113,946.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.00122 0.00010172355962756452 122.0
3 0.0015 42000 0.0047857142857142 0.0003996869775206857 200.9999999999964
4 0.002 12000 0.0196666666666666 0.001653849971735899 235.9999999999992
5 0.0025 4000 0.066 0.0056737465539274945 264.0
6 0.003 2000 0.1485 0.01330698362831062 297.0
7 0.0035 2000 0.3085 0.03027331056488236 617.0
8 0.004 2000 0.473 0.05197988715416113 946.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.626,0.07868961436921773,1252.0
0.0015,2000,0.7655,0.11384048722645845,1531.0
0.002,2000,0.8745,0.15882379851291006,1749.0
0.0025,2000,0.933,0.20168755384893544,1866.0
0.003,2000,0.972,0.2576708709890312,1944.0
0.0035,2000,0.985,0.29529459105967726,1970.0
0.004,2000,0.994,0.3471010990626149,1988.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.626 0.07868961436921773 1252.0
3 0.0015 2000 0.7655 0.11384048722645845 1531.0
4 0.002 2000 0.8745 0.15882379851291006 1749.0
5 0.0025 2000 0.933 0.20168755384893544 1866.0
6 0.003 2000 0.972 0.2576708709890312 1944.0
7 0.0035 2000 0.985 0.29529459105967726 1970.0
8 0.004 2000 0.994 0.3471010990626149 1988.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.026875,0.0022676530141574336,215.0
0.0015,4000,0.06075,0.005209184439765924,243.0
0.002,2000,0.1275,0.011301702536387737,255.0
0.0025,2000,0.2435,0.022986095764761516,487.0
0.003,2000,0.4095,0.04294919734292335,819.0
0.0035,2000,0.605,0.07448578964497943,1210.0
0.004,2000,0.767,0.11431424426031134,1534.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.026875 0.0022676530141574336 215.0
3 0.0015 4000 0.06075 0.005209184439765924 243.0
4 0.002 2000 0.1275 0.011301702536387737 255.0
5 0.0025 2000 0.2435 0.022986095764761516 487.0
6 0.003 2000 0.4095 0.04294919734292335 819.0
7 0.0035 2000 0.605 0.07448578964497943 1210.0
8 0.004 2000 0.767 0.11431424426031134 1534.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,68000,0.0030882352941176,0.00025771792946360783,209.9999999999968
0.0015,22000,0.0100454545454545,0.0008410003766037288,220.999999999999
0.002,6000,0.0353333333333333,0.0029932330235841187,211.9999999999998
0.0025,4000,0.08725,0.007578905691289939,349.0
0.003,2000,0.191,0.017507953228264928,382.0
0.0035,2000,0.3535,0.03569583157768186,707.0
0.004,2000,0.5215,0.059576452112257594,1043.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 68000 0.0030882352941176 0.00025771792946360783 209.9999999999968
3 0.0015 22000 0.0100454545454545 0.0008410003766037288 220.999999999999
4 0.002 6000 0.0353333333333333 0.0029932330235841187 211.9999999999998
5 0.0025 4000 0.08725 0.007578905691289939 349.0
6 0.003 2000 0.191 0.017507953228264928 382.0
7 0.0035 2000 0.3535 0.03569583157768186 707.0
8 0.004 2000 0.5215 0.059576452112257594 1043.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,24000,0.00875,0.0007321073812772694,210.00000000000003
0.0015,8000,0.025125,0.00211825510203556,201.0
0.002,4000,0.0815,0.0070594123157259325,326.0
0.0025,2000,0.174,0.015803830077221748,348.0
0.003,2000,0.319,0.03150899241712146,638.0
0.0035,2000,0.5135,0.05827614798780856,1027.0
0.004,2000,0.7075,0.09736849218423416,1415.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 24000 0.00875 0.0007321073812772694 210.00000000000003
3 0.0015 8000 0.025125 0.00211825510203556 201.0
4 0.002 4000 0.0815 0.0070594123157259325 326.0
5 0.0025 2000 0.174 0.015803830077221748 348.0
6 0.003 2000 0.319 0.03150899241712146 638.0
7 0.0035 2000 0.5135 0.05827614798780856 1027.0
8 0.004 2000 0.7075 0.09736849218423416 1415.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00146,0.00012174815796772709,146.0
0.0015,32000,0.0064375,0.000538047705478828,206.0
0.002,10000,0.0229,0.0019286609080385597,229.0
0.0025,4000,0.07525,0.006498116023036737,301.0
0.003,2000,0.1585,0.01427786270551501,317.0
0.0035,2000,0.3395,0.033972695445756096,679.0
0.004,2000,0.4985,0.055890042576412724,997.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.00146 0.00012174815796772709 146.0
3 0.0015 32000 0.0064375 0.000538047705478828 206.0
4 0.002 10000 0.0229 0.0019286609080385597 229.0
5 0.0025 4000 0.07525 0.006498116023036737 301.0
6 0.003 2000 0.1585 0.01427786270551501 317.0
7 0.0035 2000 0.3395 0.033972695445756096 679.0
8 0.004 2000 0.4985 0.055890042576412724 997.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.0004,3.3339446006586115e-05,40.0
0.0015,72000,0.0027916666666666,0.0002329370855657098,200.9999999999952
0.002,18000,0.0121666666666666,0.0010195870693898712,218.9999999999988
0.0025,6000,0.0435,0.0036993479983105093,261.0
0.003,4000,0.097,0.00846668118140581,388.0
0.0035,2000,0.2385,0.022449597267178878,477.0
0.004,2000,0.4015,0.04187535144908072,803.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.0004 3.3339446006586115e-05 40.0
3 0.0015 72000 0.0027916666666666 0.0002329370855657098 200.9999999999952
4 0.002 18000 0.0121666666666666 0.0010195870693898712 218.9999999999988
5 0.0025 6000 0.0435 0.0036993479983105093 261.0
6 0.003 4000 0.097 0.00846668118140581 388.0
7 0.0035 2000 0.2385 0.022449597267178878 477.0
8 0.004 2000 0.4015 0.04187535144908072 803.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.038,0.003223196672329065,228.0
0.0015,4000,0.098,0.008558231287084661,392.0
0.002,2000,0.206,0.019039074473767514,412.0
0.0025,2000,0.3485,0.035076533583668024,697.0
0.003,2000,0.5245,0.060069209055131356,1049.0
0.0035,2000,0.6985,0.09508606438098832,1397.0
0.004,2000,0.8495,0.14599310907967555,1699.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.038 0.003223196672329065 228.0
3 0.0015 4000 0.098 0.008558231287084661 392.0
4 0.002 2000 0.206 0.019039074473767514 412.0
5 0.0025 2000 0.3485 0.035076533583668024 697.0
6 0.003 2000 0.5245 0.060069209055131356 1049.0
7 0.0035 2000 0.6985 0.09508606438098832 1397.0
8 0.004 2000 0.8495 0.14599310907967555 1699.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,46000,0.004391304347826,0.0003666806266127143,201.99999999999602
0.0015,14000,0.0164285714285714,0.001379465734122176,229.9999999999996
0.002,6000,0.0438333333333333,0.003728286251850954,262.99999999999983
0.0025,2000,0.118,0.010409048871669824,236.0
0.003,2000,0.228,0.02133338177466315,456.0
0.0035,2000,0.4185,0.04417333224775788,837.0
0.004,2000,0.594,0.07236490793227202,1188.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 46000 0.004391304347826 0.0003666806266127143 201.99999999999602
3 0.0015 14000 0.0164285714285714 0.001379465734122176 229.9999999999996
4 0.002 6000 0.0438333333333333 0.003728286251850954 262.99999999999983
5 0.0025 2000 0.118 0.010409048871669824 236.0
6 0.003 2000 0.228 0.02133338177466315 456.0
7 0.0035 2000 0.4185 0.04417333224775788 837.0
8 0.004 2000 0.594 0.07236490793227202 1188.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00095,7.920115808901507e-05,95.0
0.0015,42000,0.0050238095238095,0.00041961787574185117,210.999999999999
0.002,12000,0.01975,0.0016609222901676768,237.0
0.0025,4000,0.062,0.005319578163374583,248.0
0.003,2000,0.159,0.014326683792962536,318.0
0.0035,2000,0.313,0.030800767790453154,626.0
0.004,2000,0.47,0.0515313313739999,940.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.00095 7.920115808901507e-05 95.0
3 0.0015 42000 0.0050238095238095 0.00041961787574185117 210.999999999999
4 0.002 12000 0.01975 0.0016609222901676768 237.0
5 0.0025 4000 0.062 0.005319578163374583 248.0
6 0.003 2000 0.159 0.014326683792962536 318.0
7 0.0035 2000 0.313 0.030800767790453154 626.0
8 0.004 2000 0.47 0.0515313313739999 940.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.626,0.07868961436921773,1252.0
0.0015,2000,0.7655,0.11384048722645845,1531.0
0.002,2000,0.8745,0.15882379851291006,1749.0
0.0025,2000,0.933,0.20168755384893544,1866.0
0.003,2000,0.972,0.2576708709890312,1944.0
0.0035,2000,0.985,0.29529459105967726,1970.0
0.004,2000,0.994,0.3471010990626149,1988.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.626 0.07868961436921773 1252.0
3 0.0015 2000 0.7655 0.11384048722645845 1531.0
4 0.002 2000 0.8745 0.15882379851291006 1749.0
5 0.0025 2000 0.933 0.20168755384893544 1866.0
6 0.003 2000 0.972 0.2576708709890312 1944.0
7 0.0035 2000 0.985 0.29529459105967726 1970.0
8 0.004 2000 0.994 0.3471010990626149 1988.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.026125,0.0022035952056765895,209.0
0.0015,4000,0.06075,0.005209184439765924,243.0
0.002,2000,0.136,0.012107977177767903,272.0
0.0025,2000,0.254,0.02412340479098629,508.0
0.003,2000,0.4115,0.043219741997103434,823.0
0.0035,2000,0.6,0.07351512752093081,1200.0
0.004,2000,0.7645,0.11352619006706066,1529.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.026125 0.0022035952056765895 209.0
3 0.0015 4000 0.06075 0.005209184439765924 243.0
4 0.002 2000 0.136 0.012107977177767903 272.0
5 0.0025 2000 0.254 0.02412340479098629 508.0
6 0.003 2000 0.4115 0.043219741997103434 823.0
7 0.0035 2000 0.6 0.07351512752093081 1200.0
8 0.004 2000 0.7645 0.11352619006706066 1529.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,72000,0.0027777777777777,0.00023177671578233916,199.9999999999944
0.0015,20000,0.0105,0.0008792394039432994,210.0
0.002,8000,0.032125,0.0027173290492218394,257.0
0.0025,4000,0.08575,0.007443097095222506,343.0
0.003,2000,0.186,0.01700335914772977,372.0
0.0035,2000,0.356,0.03600712878727563,712.0
0.004,2000,0.529,0.06081371425997428,1058.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 72000 0.0027777777777777 0.00023177671578233916 199.9999999999944
3 0.0015 20000 0.0105 0.0008792394039432994 210.0
4 0.002 8000 0.032125 0.0027173290492218394 257.0
5 0.0025 4000 0.08575 0.007443097095222506 343.0
6 0.003 2000 0.186 0.01700335914772977 372.0
7 0.0035 2000 0.356 0.03600712878727563 712.0
8 0.004 2000 0.529 0.06081371425997428 1058.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0453333333333333,0.0038586229394146354,271.99999999999983
0.0015,2000,0.1245,0.011018853369859305,249.0
0.002,2000,0.2185,0.02033539996612399,437.0
0.0025,2000,0.3975,0.041343353576980935,795.0
0.003,2000,0.5945,0.07246016235632424,1189.0
0.0035,2000,0.735,0.1047647873005133,1470.0
0.004,2000,0.8745,0.15882379851291006,1749.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0453333333333333 0.0038586229394146354 271.99999999999983
3 0.0015 2000 0.1245 0.011018853369859305 249.0
4 0.002 2000 0.2185 0.02033539996612399 437.0
5 0.0025 2000 0.3975 0.041343353576980935 795.0
6 0.003 2000 0.5945 0.07246016235632424 1189.0
7 0.0035 2000 0.735 0.1047647873005133 1470.0
8 0.004 2000 0.8745 0.15882379851291006 1749.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.030875,0.00261006088942628,247.0
0.0015,4000,0.07825,0.006767102824702054,313.0
0.002,2000,0.141,0.012585659483247746,282.0
0.0025,2000,0.279,0.02689148662280816,558.0
0.003,2000,0.4385,0.046957034683799304,877.0
0.0035,2000,0.633,0.08013907230132367,1266.0
0.004,2000,0.793,0.12300416913096102,1586.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.030875 0.00261006088942628 247.0
3 0.0015 4000 0.07825 0.006767102824702054 313.0
4 0.002 2000 0.141 0.012585659483247746 282.0
5 0.0025 2000 0.279 0.02689148662280816 558.0
6 0.003 2000 0.4385 0.046957034683799304 877.0
7 0.0035 2000 0.633 0.08013907230132367 1266.0
8 0.004 2000 0.793 0.12300416913096102 1586.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.027625,0.0023317560946333193,221.0
0.0015,4000,0.06525,0.005607234208600653,261.0
0.002,2000,0.122,0.010783823589648356,244.0
0.0025,2000,0.2335,0.021916318194268203,467.0
0.003,2000,0.385,0.03970147975050575,770.0
0.0035,2000,0.569,0.0677341570379616,1138.0
0.004,2000,0.729,0.10309294344737896,1458.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.027625 0.0023317560946333193 221.0
3 0.0015 4000 0.06525 0.005607234208600653 261.0
4 0.002 2000 0.122 0.010783823589648356 244.0
5 0.0025 2000 0.2335 0.021916318194268203 467.0
6 0.003 2000 0.385 0.03970147975050575 770.0
7 0.0035 2000 0.569 0.0677341570379616 1138.0
8 0.004 2000 0.729 0.10309294344737896 1458.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.122,0.010783823589648356,244.0
0.0015,2000,0.2475,0.02341764001219704,495.0
0.002,2000,0.38,0.039053282833609426,760.0
0.0025,2000,0.5705,0.06800496801270284,1141.0
0.003,2000,0.7255,0.10213330493021633,1451.0
0.0035,2000,0.846,0.14435544028130065,1692.0
0.004,2000,0.944,0.2135296839449985,1888.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.122 0.010783823589648356 244.0
3 0.0015 2000 0.2475 0.02341764001219704 495.0
4 0.002 2000 0.38 0.039053282833609426 760.0
5 0.0025 2000 0.5705 0.06800496801270284 1141.0
6 0.003 2000 0.7255 0.10213330493021633 1451.0
7 0.0035 2000 0.846 0.14435544028130065 1692.0
8 0.004 2000 0.944 0.2135296839449985 1888.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0375,0.00318003401506195,225.0
0.0015,4000,0.08775,0.007624220689530503,351.0
0.002,2000,0.169,0.015308735184581312,338.0
0.0025,2000,0.3185,0.03144975567894859,637.0
0.003,2000,0.4945,0.055264800927331104,989.0
0.0035,2000,0.6715,0.08859526368715209,1343.0
0.004,2000,0.8295,0.13706709042620446,1659.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0375 0.00318003401506195 225.0
3 0.0015 4000 0.08775 0.007624220689530503 351.0
4 0.002 2000 0.169 0.015308735184581312 338.0
5 0.0025 2000 0.3185 0.03144975567894859 637.0
6 0.003 2000 0.4945 0.055264800927331104 989.0
7 0.0035 2000 0.6715 0.08859526368715209 1343.0
8 0.004 2000 0.8295 0.13706709042620446 1659.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.029125,0.0024600983395249854,233.0
0.0015,4000,0.06525,0.005607234208600653,261.0
0.002,2000,0.129,0.011443461592906767,258.0
0.0025,2000,0.2545,0.02417792760750781,509.0
0.003,2000,0.416,0.043831562260356005,832.0
0.0035,2000,0.5905,0.07170112206446477,1181.0
0.004,2000,0.763,0.1130570306237979,1526.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.029125 0.0024600983395249854 233.0
3 0.0015 4000 0.06525 0.005607234208600653 261.0
4 0.002 2000 0.129 0.011443461592906767 258.0
5 0.0025 2000 0.2545 0.02417792760750781 509.0
6 0.003 2000 0.416 0.043831562260356005 832.0
7 0.0035 2000 0.5905 0.07170112206446477 1181.0
8 0.004 2000 0.763 0.1130570306237979 1526.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.789,0.12160429293407071,1578.0
0.0015,2000,0.9,0.1745958147319816,1800.0
0.002,2000,0.9465,0.21651717275071503,1893.0
0.0025,2000,0.967,0.24743705884517853,1934.0
0.003,2000,0.9905,0.32161385879719506,1981.0
0.0035,2000,0.9965,0.3757780964762649,1993.0
0.004,2000,0.9995,0.4692204681934514,1999.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.789 0.12160429293407071 1578.0
3 0.0015 2000 0.9 0.1745958147319816 1800.0
4 0.002 2000 0.9465 0.21651717275071503 1893.0
5 0.0025 2000 0.967 0.24743705884517853 1934.0
6 0.003 2000 0.9905 0.32161385879719506 1981.0
7 0.0035 2000 0.9965 0.3757780964762649 1993.0
8 0.004 2000 0.9995 0.4692204681934514 1999.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.09675,0.008443808176524459,387.0
0.0015,2000,0.1775,0.01615203373482954,355.0
0.002,2000,0.322,0.03186525232867321,644.0
0.0025,2000,0.4605,0.050126101092695885,921.0
0.003,2000,0.653,0.08442458415488852,1306.0
0.0035,2000,0.798,0.12478930891509032,1596.0
0.004,2000,0.912,0.18334199643064264,1824.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.09675 0.008443808176524459 387.0
3 0.0015 2000 0.1775 0.01615203373482954 355.0
4 0.002 2000 0.322 0.03186525232867321 644.0
5 0.0025 2000 0.4605 0.050126101092695885 921.0
6 0.003 2000 0.653 0.08442458415488852 1306.0
7 0.0035 2000 0.798 0.12478930891509032 1596.0
8 0.004 2000 0.912 0.18334199643064264 1824.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0365,0.0030937703263473892,219.0
0.0015,4000,0.0795,0.006879417576947544,318.0
0.002,2000,0.1575,0.01418030025167627,315.0
0.0025,2000,0.29,0.028137416075114108,580.0
0.003,2000,0.4455,0.04795283848945675,891.0
0.0035,2000,0.6305,0.07961852200020059,1261.0
0.004,2000,0.7825,0.11938055324065988,1565.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0365 0.0030937703263473892 219.0
3 0.0015 4000 0.0795 0.006879417576947544 318.0
4 0.002 2000 0.1575 0.01418030025167627 315.0
5 0.0025 2000 0.29 0.028137416075114108 580.0
6 0.003 2000 0.4455 0.04795283848945675 891.0
7 0.0035 2000 0.6305 0.07961852200020059 1261.0
8 0.004 2000 0.7825 0.11938055324065988 1565.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0343333333333333,0.0029071468641445053,205.9999999999998
0.0015,4000,0.0885,0.0076922358935922475,354.0
0.002,2000,0.177,0.0161022072935475,354.0
0.0025,2000,0.3325,0.03312364612025187,665.0
0.003,2000,0.501,0.05628314409130197,1002.0
0.0035,2000,0.682,0.09105921022136998,1364.0
0.004,2000,0.8345,0.13920480678485292,1669.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0343333333333333 0.0029071468641445053 205.9999999999998
3 0.0015 4000 0.0885 0.0076922358935922475 354.0
4 0.002 2000 0.177 0.0161022072935475 354.0
5 0.0025 2000 0.3325 0.03312364612025187 665.0
6 0.003 2000 0.501 0.05628314409130197 1002.0
7 0.0035 2000 0.682 0.09105921022136998 1364.0
8 0.004 2000 0.8345 0.13920480678485292 1669.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,10000,0.0225,0.0018946185336699006,225.0
0.0015,4000,0.05,0.004265318777560645,200.0
0.002,2000,0.1095,0.009617798287998358,219.0
0.0025,2000,0.214,0.01986654747829364,428.0
0.003,2000,0.364,0.03701077827175081,728.0
0.0035,2000,0.5525,0.06481093518102832,1105.0
0.004,2000,0.7365,0.10518816757921234,1473.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 10000 0.0225 0.0018946185336699006 225.0
3 0.0015 4000 0.05 0.004265318777560645 200.0
4 0.002 2000 0.1095 0.009617798287998358 219.0
5 0.0025 2000 0.214 0.01986654747829364 428.0
6 0.003 2000 0.364 0.03701077827175081 728.0
7 0.0035 2000 0.5525 0.06481093518102832 1105.0
8 0.004 2000 0.7365 0.10518816757921234 1473.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,12000,0.0199166666666666,0.0016750685805796417,238.9999999999992
0.0015,6000,0.0413333333333333,0.0035114743705089158,247.9999999999998
0.002,4000,0.082,0.007104467133977943,328.0
0.0025,2000,0.194,0.01781208360090769,388.0
0.003,2000,0.321,0.03174633874742727,642.0
0.0035,2000,0.4975,0.055733304754966406,995.0
0.004,2000,0.6875,0.0923797676224748,1375.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 12000 0.0199166666666666 0.0016750685805796417 238.9999999999992
3 0.0015 6000 0.0413333333333333 0.0035114743705089158 247.9999999999998
4 0.002 4000 0.082 0.007104467133977943 328.0
5 0.0025 2000 0.194 0.01781208360090769 388.0
6 0.003 2000 0.321 0.03174633874742727 642.0
7 0.0035 2000 0.4975 0.055733304754966406 995.0
8 0.004 2000 0.6875 0.0923797676224748 1375.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.124,0.010971798240880681,248.0
0.0015,2000,0.2365,0.0222359015716157,473.0
0.002,2000,0.3665,0.037326792598442404,733.0
0.0025,2000,0.5595,0.06603881814539603,1119.0
0.003,2000,0.73,0.10336921268218224,1460.0
0.0035,2000,0.837,0.14029596115963894,1674.0
0.004,2000,0.9355,0.20421336158924952,1871.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.124 0.010971798240880681 248.0
3 0.0015 2000 0.2365 0.0222359015716157 473.0
4 0.002 2000 0.3665 0.037326792598442404 733.0
5 0.0025 2000 0.5595 0.06603881814539603 1119.0
6 0.003 2000 0.73 0.10336921268218224 1460.0
7 0.0035 2000 0.837 0.14029596115963894 1674.0
8 0.004 2000 0.9355 0.20421336158924952 1871.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.0295,0.002492212300538421,236.0
0.0015,4000,0.07525,0.006498116023036737,301.0
0.002,2000,0.154,0.013839665569208792,308.0
0.0025,2000,0.293,0.02848028572607786,586.0
0.003,2000,0.4585,0.04983315584200687,917.0
0.0035,2000,0.6525,0.08431471728347295,1305.0
0.004,2000,0.802,0.1262468270496201,1604.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.0295 0.002492212300538421 236.0
3 0.0015 4000 0.07525 0.006498116023036737 301.0
4 0.002 2000 0.154 0.013839665569208792 308.0
5 0.0025 2000 0.293 0.02848028572607786 586.0
6 0.003 2000 0.4585 0.04983315584200687 917.0
7 0.0035 2000 0.6525 0.08431471728347295 1305.0
8 0.004 2000 0.802 0.1262468270496201 1604.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.025875,0.0021822526517427665,207.0
0.0015,4000,0.0535,0.004571544229555857,214.0
0.002,2000,0.1165,0.010268909922777514,233.0
0.0025,2000,0.231,0.021650873371036106,462.0
0.003,2000,0.3665,0.037326792598442404,733.0
0.0035,2000,0.552,0.06472390440895348,1104.0
0.004,2000,0.742,0.10675969983223876,1484.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.025875 0.0021822526517427665 207.0
3 0.0015 4000 0.0535 0.004571544229555857 214.0
4 0.002 2000 0.1165 0.010268909922777514 233.0
5 0.0025 2000 0.231 0.021650873371036106 462.0
6 0.003 2000 0.3665 0.037326792598442404 733.0
7 0.0035 2000 0.552 0.06472390440895348 1104.0
8 0.004 2000 0.742 0.10675969983223876 1484.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.789,0.12160429293407071,1578.0
0.0015,2000,0.9,0.1745958147319816,1800.0
0.002,2000,0.9465,0.21651717275071503,1893.0
0.0025,2000,0.967,0.24743705884517853,1934.0
0.003,2000,0.9905,0.32161385879719506,1981.0
0.0035,2000,0.9965,0.3757780964762649,1993.0
0.004,2000,0.9995,0.4692204681934514,1999.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.789 0.12160429293407071 1578.0
3 0.0015 2000 0.9 0.1745958147319816 1800.0
4 0.002 2000 0.9465 0.21651717275071503 1893.0
5 0.0025 2000 0.967 0.24743705884517853 1934.0
6 0.003 2000 0.9905 0.32161385879719506 1981.0
7 0.0035 2000 0.9965 0.3757780964762649 1993.0
8 0.004 2000 0.9995 0.4692204681934514 1999.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0915,0.007964810720254789,366.0
0.0015,2000,0.18,0.016401583188387914,360.0
0.002,2000,0.307,0.03009819055291696,614.0
0.0025,2000,0.4545,0.04925022878399943,909.0
0.003,2000,0.649,0.08354968174320077,1298.0
0.0035,2000,0.793,0.12300416913096102,1586.0
0.004,2000,0.9115,0.18295632456593924,1823.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0915 0.007964810720254789 366.0
3 0.0015 2000 0.18 0.016401583188387914 360.0
4 0.002 2000 0.307 0.03009819055291696 614.0
5 0.0025 2000 0.4545 0.04925022878399943 909.0
6 0.003 2000 0.649 0.08354968174320077 1298.0
7 0.0035 2000 0.793 0.12300416913096102 1586.0
8 0.004 2000 0.9115 0.18295632456593924 1823.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.030125,0.002545760854709589,241.0
0.0015,4000,0.067,0.005762505879780444,268.0
0.002,2000,0.1355,0.012060348411758404,271.0
0.0025,2000,0.2805,0.027060355839749417,561.0
0.003,2000,0.4395,0.0470985932750948,879.0
0.0035,2000,0.619,0.07726481455474521,1238.0
0.004,2000,0.7745,0.11672579914287295,1549.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.030125 0.002545760854709589 241.0
3 0.0015 4000 0.067 0.005762505879780444 268.0
4 0.002 2000 0.1355 0.012060348411758404 271.0
5 0.0025 2000 0.2805 0.027060355839749417 561.0
6 0.003 2000 0.4395 0.0470985932750948 879.0
7 0.0035 2000 0.619 0.07726481455474521 1238.0
8 0.004 2000 0.7745 0.11672579914287295 1549.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.115,0.010128988904076097,230.0
0.0015,2000,0.2165,0.020126716372619535,433.0
0.002,2000,0.3575,0.0361944392516631,715.0
0.0025,2000,0.5255,0.06023409479070929,1051.0
0.003,2000,0.6935,0.09384489827464226,1387.0
0.0035,2000,0.816,0.13156999840650407,1632.0
0.004,2000,0.9105,0.1821909360735222,1821.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.115 0.010128988904076097 230.0
3 0.0015 2000 0.2165 0.020126716372619535 433.0
4 0.002 2000 0.3575 0.0361944392516631 715.0
5 0.0025 2000 0.5255 0.06023409479070929 1051.0
6 0.003 2000 0.6935 0.09384489827464226 1387.0
7 0.0035 2000 0.816 0.13156999840650407 1632.0
8 0.004 2000 0.9105 0.1821909360735222 1821.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0975,0.008512444610847103,390.0
0.0015,2000,0.1915,0.017558569754261066,383.0
0.002,2000,0.2765,0.02661075227253118,553.0
0.0025,2000,0.448,0.04831127709115113,896.0
0.003,2000,0.5865,0.07094884804525436,1173.0
0.0035,2000,0.7455,0.10777583350900755,1491.0
0.004,2000,0.8585,0.15037026489320615,1717.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0975 0.008512444610847103 390.0
3 0.0015 2000 0.1915 0.017558569754261066 383.0
4 0.002 2000 0.2765 0.02661075227253118 553.0
5 0.0025 2000 0.448 0.04831127709115113 896.0
6 0.003 2000 0.5865 0.07094884804525436 1173.0
7 0.0035 2000 0.7455 0.10777583350900755 1491.0
8 0.004 2000 0.8585 0.15037026489320615 1717.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0925,0.008055852365631777,370.0
0.0015,2000,0.1735,0.015754197146499838,347.0
0.002,2000,0.265,0.025330719468954155,530.0
0.0025,2000,0.427,0.04534551221126004,854.0
0.003,2000,0.571,0.0680954310203834,1142.0
0.0035,2000,0.7105,0.09814362266376564,1421.0
0.004,2000,0.8315,0.1379151915045972,1663.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0925 0.008055852365631777 370.0
3 0.0015 2000 0.1735 0.015754197146499838 347.0
4 0.002 2000 0.265 0.025330719468954155 530.0
5 0.0025 2000 0.427 0.04534551221126004 854.0
6 0.003 2000 0.571 0.0680954310203834 1142.0
7 0.0035 2000 0.7105 0.09814362266376564 1421.0
8 0.004 2000 0.8315 0.1379151915045972 1663.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.189,0.01730577346851303,378.0
0.0015,2000,0.334,0.03330489586414709,668.0
0.002,2000,0.462,0.050346464045528894,924.0
0.0025,2000,0.67,0.088249181932055,1340.0
0.003,2000,0.8035,0.12680036354194668,1607.0
0.0035,2000,0.8915,0.1689653255579383,1783.0
0.004,2000,0.965,0.2437378987592076,1930.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.189 0.01730577346851303 378.0
3 0.0015 2000 0.334 0.03330489586414709 668.0
4 0.002 2000 0.462 0.050346464045528894 924.0
5 0.0025 2000 0.67 0.088249181932055 1340.0
6 0.003 2000 0.8035 0.12680036354194668 1607.0
7 0.0035 2000 0.8915 0.1689653255579383 1783.0
8 0.004 2000 0.965 0.2437378987592076 1930.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.092,0.008010320054115394,368.0
0.0015,2000,0.182,0.016601725400650635,364.0
0.002,2000,0.2885,0.027966478964539188,577.0
0.0025,2000,0.468,0.05123358540561418,936.0
0.003,2000,0.6195,0.07736578684966466,1239.0
0.0035,2000,0.7805,0.11870857647232991,1561.0
0.004,2000,0.8815,0.16283731439217686,1763.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.092 0.008010320054115394 368.0
3 0.0015 2000 0.182 0.016601725400650635 364.0
4 0.002 2000 0.2885 0.027966478964539188 577.0
5 0.0025 2000 0.468 0.05123358540561418 936.0
6 0.003 2000 0.6195 0.07736578684966466 1239.0
7 0.0035 2000 0.7805 0.11870857647232991 1561.0
8 0.004 2000 0.8815 0.16283731439217686 1763.0

View File

@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0825,0.007149544452537682,330.0
0.0015,2000,0.1585,0.01427786270551501,317.0
0.002,2000,0.2535,0.024068915462335805,507.0
0.0025,2000,0.4035,0.042142573743546796,807.0
0.003,2000,0.5605,0.06621568805942701,1121.0
0.0035,2000,0.729,0.10309294344737896,1458.0
0.004,2000,0.8435,0.14320643674428069,1687.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0825 0.007149544452537682 330.0
3 0.0015 2000 0.1585 0.01427786270551501 317.0
4 0.002 2000 0.2535 0.024068915462335805 507.0
5 0.0025 2000 0.4035 0.042142573743546796 807.0
6 0.003 2000 0.5605 0.06621568805942701 1121.0
7 0.0035 2000 0.729 0.10309294344737896 1458.0
8 0.004 2000 0.8435 0.14320643674428069 1687.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.818,0.13236056607309032,1636.0
0.0015,2000,0.901,0.17528682442801136,1802.0
0.002,2000,0.9565,0.2299112633774043,1913.0
0.0025,2000,0.969,0.25134773793289455,1938.0
0.003,2000,0.9945,0.3518181130178767,1989.0
0.0035,2000,0.997,0.3837454986270925,1994.0
0.004,2000,0.9995,0.4692204681934514,1999.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.818 0.13236056607309032 1636.0
3 0.0015 2000 0.901 0.17528682442801136 1802.0
4 0.002 2000 0.9565 0.2299112633774043 1913.0
5 0.0025 2000 0.969 0.25134773793289455 1938.0
6 0.003 2000 0.9945 0.3518181130178767 1989.0
7 0.0035 2000 0.997 0.3837454986270925 1994.0
8 0.004 2000 0.9995 0.4692204681934514 1999.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.146,0.013065897372720348,292.0
0.0015,2000,0.2805,0.027060355839749417,561.0
0.002,2000,0.415,0.043695229663312296,830.0
0.0025,2000,0.578,0.06937216612000952,1156.0
0.003,2000,0.746,0.10792203989196847,1492.0
0.0035,2000,0.8665,0.15448086847325826,1733.0
0.004,2000,0.9405,0.2095463416012857,1881.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.146 0.013065897372720348 292.0
3 0.0015 2000 0.2805 0.027060355839749417 561.0
4 0.002 2000 0.415 0.043695229663312296 830.0
5 0.0025 2000 0.578 0.06937216612000952 1156.0
6 0.003 2000 0.746 0.10792203989196847 1492.0
7 0.0035 2000 0.8665 0.15448086847325826 1733.0
8 0.004 2000 0.9405 0.2095463416012857 1881.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.082,0.007104467133977943,328.0
0.0015,2000,0.1555,0.013985493383097625,311.0
0.002,2000,0.26,0.024779901164930007,520.0
0.0025,2000,0.434,0.04632286722747814,868.0
0.003,2000,0.603,0.07409618065132939,1206.0
0.0035,2000,0.7465,0.10806851033720544,1493.0
0.004,2000,0.859,0.1506208564330962,1718.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.082 0.007104467133977943 328.0
3 0.0015 2000 0.1555 0.013985493383097625 311.0
4 0.002 2000 0.26 0.024779901164930007 520.0
5 0.0025 2000 0.434 0.04632286722747814 868.0
6 0.003 2000 0.603 0.07409618065132939 1206.0
7 0.0035 2000 0.7465 0.10806851033720544 1493.0
8 0.004 2000 0.859 0.1506208564330962 1718.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.058,0.004966791530059078,232.0
0.0015,2000,0.129,0.011443461592906767,258.0
0.002,2000,0.241,0.022717441549556572,482.0
0.0025,2000,0.4295,0.04569330484413092,859.0
0.003,2000,0.593,0.07217472124714996,1186.0
0.0035,2000,0.744,0.10733878882764858,1488.0
0.004,2000,0.871,0.15689342561956476,1742.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.058 0.004966791530059078 232.0
3 0.0015 2000 0.129 0.011443461592906767 258.0
4 0.002 2000 0.241 0.022717441549556572 482.0
5 0.0025 2000 0.4295 0.04569330484413092 859.0
6 0.003 2000 0.593 0.07217472124714996 1186.0
7 0.0035 2000 0.744 0.10733878882764858 1488.0
8 0.004 2000 0.871 0.15689342561956476 1742.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0401666666666666,0.0034104728062255285,240.9999999999996
0.0015,4000,0.08925,0.007760302417565645,357.0
0.002,2000,0.156,0.014034155420617034,312.0
0.0025,2000,0.3135,0.030859569505892193,627.0
0.003,2000,0.445,0.0478813285765477,890.0
0.0035,2000,0.63,0.07951479949867513,1260.0
0.004,2000,0.795,0.12371343129766765,1590.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0401666666666666 0.0034104728062255285 240.9999999999996
3 0.0015 4000 0.08925 0.007760302417565645 357.0
4 0.002 2000 0.156 0.014034155420617034 312.0
5 0.0025 2000 0.3135 0.030859569505892193 627.0
6 0.003 2000 0.445 0.0478813285765477 890.0
7 0.0035 2000 0.63 0.07951479949867513 1260.0
8 0.004 2000 0.795 0.12371343129766765 1590.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.032,0.002706596520449467,256.0
0.0015,4000,0.074,0.006386274207228704,296.0
0.002,2000,0.1445,0.012921555968088194,289.0
0.0025,2000,0.275,0.02644273818893983,550.0
0.003,2000,0.411,0.043152026910574515,822.0
0.0035,2000,0.5955,0.07265099463809832,1191.0
0.004,2000,0.7595,0.11197282364335293,1519.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.032 0.002706596520449467 256.0
3 0.0015 4000 0.074 0.006386274207228704 296.0
4 0.002 2000 0.1445 0.012921555968088194 289.0
5 0.0025 2000 0.275 0.02644273818893983 550.0
6 0.003 2000 0.411 0.043152026910574515 822.0
7 0.0035 2000 0.5955 0.07265099463809832 1191.0
8 0.004 2000 0.7595 0.11197282364335293 1519.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,2000,0.1595,0.014375531494479343,319.0
0.0015,2000,0.3005,0.029343329924881867,601.0
0.002,2000,0.4525,0.04896023310758335,905.0
0.0025,2000,0.6415,0.08193359243128073,1283.0
0.003,2000,0.7785,0.11804218900471797,1557.0
0.0035,2000,0.879,0.16137955205786958,1758.0
0.004,2000,0.9555,0.22845131704956945,1911.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.1595 0.014375531494479343 319.0
3 0.0015 2000 0.3005 0.029343329924881867 601.0
4 0.002 2000 0.4525 0.04896023310758335 905.0
5 0.0025 2000 0.6415 0.08193359243128073 1283.0
6 0.003 2000 0.7785 0.11804218900471797 1557.0
7 0.0035 2000 0.879 0.16137955205786958 1758.0
8 0.004 2000 0.9555 0.22845131704956945 1911.0

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