29 Commits

Author SHA1 Message Date
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
415 changed files with 6240 additions and 839 deletions

View File

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

View File

@@ -8,16 +8,31 @@
long=belief propagation
}
\DeclareAcronym{bpgd}{
short=BPGD,
long=belief propagation with guided decimation
}
\DeclareAcronym{nms}{
short=NMS,
long=normalized min-sum
}
\DeclareAcronym{bsc}{
short=BSC,
long=binary symetric channel
}
\DeclareAcronym{spa}{
short=SPA,
long=sum-product algorithm
}
\DeclareAcronym{css}{
short=CSS,
long=Calderbank-Shor-Steane
}
\DeclareAcronym{llr}{
short=LLR,
long=log-likelihood ratio
@@ -33,6 +48,11 @@
long=low-density parity-check
}
\DeclareAcronym{qldpc}{
short=QLDPC,
long=quantum low-density parity-check
}
\DeclareAcronym{ml}{
short=ML,
long=maximum likelihood
@@ -77,3 +97,8 @@
short=PDF,
long=probability density function
}
\DeclareAcronym{bb}{
short=BB,
long=bivariate bicycle
}

File diff suppressed because it is too large Load Diff

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@@ -1,4 +1,4 @@
\chapter{Fundamentals}
\chapter{Fundamentals of Classical and Quantum Error Correction}
\label{ch:Fundamentals}
\Ac{qec} is a field of research combining ``classical''
@@ -6,8 +6,6 @@ communications engineering and quantum information science.
This chapter provides the relevant theoretical background on both of
these topics and subsequently introduces the fundamentals of \ac{qec}.
% TODO: Is an explanation of BP with guided decimation needed in this chapter?
% TODO: Is an explanation of OSD needed chapter?
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Classical Error Correction}
\label{sec:Classical Error Correction}
@@ -517,6 +515,7 @@ This is precisely the effect that leads to the good performance of
\ac{sc}-\ac{ldpc} codes in the waterfall region \cite{costello_spatially_2014}.
\subsection{Iterative Decoding}
\label{subsec:Iterative Decoding}
% Introduction
@@ -607,21 +606,23 @@ The purpose of this section is to convey these concepts to the reader.
In quantum mechanics, the state of a particle is described by a
\emph{wave function} $\psi(x,t)$.
The connection between this function and the observable world
is Born's statistical interpretation:
$\lvert \psi (x,t) \rvert^2$ is the \ac{pdf} of finding a praticle at
Born's statistical interpretation provides a connection between this
function and the observable world:
$\lvert \psi (x,t) \rvert^2$ is the \ac{pdf} of finding a particle at
position $x$ and time $t$ \cite[Sec.~1.2]{griffiths_introduction_1995}.
Note that this presupposes a normalization of $\psi$ such that
$\int_{-\infty}^{\infty} \lvert \psi(x,t) \rvert^2 dx = 1$.
% Dirac notation
A lot of the related mathematics can be very elegantly expressed
Much of the related mathematics can be very elegantly expressed
using the language of linear algebra.
The so called Bra-ket or Dirac notation is especially appropriate,
The so-called Bra-ket or Dirac notation is especially appropriate,
having been proposed by Paul Dirac in 1939 for the express purpose
of simplifying quantum mechanical notation \cite{dirac_new_1939}.
Two new symbols are defined, \emph{bra}s $\bra{\cdot}$ and
\emph{ket}s $\ket{\cdot}$.
Kets denote ordinary vectors, while bras denote their Hermitian conjugates.
Kets denote column vectors, while bras denote their Hermitian conjugates.
For example, two vectors specified by the labels $a$ and $b$
respectively are written as $\ket{a}$ and $\ket{b}$.
Their inner product is $\braket{a\vert b}$.
@@ -630,7 +631,7 @@ Their inner product is $\braket{a\vert b}$.
The connection we will make between quantum mechanics and linear
algebra is that we will model the state space of a system as a
\emph{function space}.
\emph{function space}, the Hilbert space $L_2$.
We will represent the state of a particle with wave function
$\psi(x,t)$ using the vector $\ket{\psi}$
\cite[Sec.~3.3]{griffiths_introduction_1995}.
@@ -643,6 +644,17 @@ output \cite[Sec.~3.2.2]{griffiths_introduction_1995}.
Operators are useful to describe the relations between different
quantities relating to a particle.
An example of this is the differential operator $\partial x$.
We define the \emph{commutator} of two operators $P_1$ and $P_2$ as
\begin{align*}
[P_1,P_2] = P_1P_2 - P_2P_1
\end{align*}
and the \emph{anticommutator} as
\begin{align*}
[P_1,P_2]_+ = P_1P_2 + P_2P_1
.%
\end{align*}
We say the two operators \emph{commute} iff $[P_1,P_2] = 0$, and they
\emph{anti-commute} iff $[P_1,P_2]_+ = 0$.
%%%%%%%%%%%%%%%%
\subsection{Observables}
@@ -663,8 +675,8 @@ observations \cite[Sec.~3.3]{griffiths_introduction_1995}.
If we know the wave function of a particle, we should be able to
compute the expected value $\braket{Q}$ of any observable quantity we wish.
It can be shown that for any $Q$, we can compute a
corresponding operator $\hat{Q}$ such that
It can be shown that for any $Q$, we can find a
corresponding Hermitian operator $\hat{Q}$ such that
\cite[Sec.~3.3]{griffiths_introduction_1995}
\begin{align}
\label{eq:gen_expr_Q_exp}
@@ -689,7 +701,7 @@ formula simplifies to the direct calculation of the expected value.
Let us now examine how the observable operator $\hat{Q}$ relates to
the determinate states of the observable quantity.
We begin by translating \autoref{eq:gen_expr_Q_exp} into linear alebra as
We begin by translating \autoref{eq:gen_expr_Q_exp} into linear algebra as
\cite[Eq.~3.114]{griffiths_introduction_1995}
\begin{align}
\label{eq:gen_expr_Q_exp_lin}
@@ -697,7 +709,7 @@ We begin by translating \autoref{eq:gen_expr_Q_exp} into linear alebra as
.%
\end{align}
\autoref{eq:gen_expr_Q_exp_lin} expresses an inherently probabilistic
relationhip.
relationship.
The determinate states are inherently deterministic.
To relate the two, we note that since determinate states should
always yield the same measurement results, the variance of the
@@ -742,6 +754,9 @@ We can use the determinate states for this purpose, expressing the state as%
c_n := \braket{e_n \vert \psi}
.%
\end{align}
Because of the normalization of the wave function such that
$\int_{-\infty}^{\infty} \lvert \psi(x,t) \rvert^2 dx = 1$, we have
$\sum_{n=1}^{\infty} \lvert c_n \rvert ^2 = 1$.
Inserting \autoref{eq:determinate_basis} into
\autoref{eq:gen_expr_Q_exp_lin} we obtain
% tex-fmt: off
@@ -771,7 +786,7 @@ We can decompose an arbitrary state as $\ket{\psi} = \sum_{n=1}^{\infty} c_n
\ket{e_n}$, where $\lvert c_n \rvert ^2$ represents the probability
of obtaining a certain measurement value.
Note that when we speak of an \emph{observable}, we are usually
refering to the operator $\hat{Q}$.
referring to the operator $\hat{Q}$.
%%%%%%%%%%%%%%%%
\subsection{Projective Measurements}
@@ -837,7 +852,7 @@ These project a vector onto the subspace spanned by $\ket{e_n}$.
\subsection{Qubits and Multi-Qubit States}
\label{subsec:Qubits and Multi-Qubit States}
% The qubit
% Intro
% TODO: Make sure `quantum gate` is proper terminology
A central concept for quantum computing is that of the \emph{qubit}.
@@ -846,20 +861,35 @@ For classical computers, we alter bits' states using \emph{gates}.
We can chain multiple of these gates together to build up more complex logic,
such as half-adders or eventually a full processor.
In principle, quantum computers work in a similar fashion, only that
instead of bits we use qubits and instead of, e.g. {AND}, OR, and XOR
instead of bits we use qubits and instead of, e.g., AND, OR, and XOR
operations we use \emph{quantum gates} \cite[Sec.~1.3]{nielsen_quantum_2010}.
We define a qubit to be a component with determinate
states $\ket{0}$ and $\ket{1}$.
The general description of the state $\ket{\psi}$ of a qubit is thus
\begin{align}
\label{eq:gen_qubit_state}
\ket{\psi} = \alpha\ket{0} + \beta\ket{1}, \hspace{5mm} \alpha,
\beta \in \mathbb{C}
% Qubits and multi-qubit states
We fix an orthonormal basis of $\mathbb{C}^2$ to be
\begin{align*}
\ket{0} =
\begin{pmatrix}
1 \\
0
\end{pmatrix}, \hspace{5mm}
\ket{1} =
\begin{pmatrix}
0 \\
1
\end{pmatrix}
.%
\end{align}
% The tensor product and multi-qubit states
\end{align*}
A qubit is defined to be a system with quantum state
\begin{align*}
\ket{\psi} =
\begin{pmatrix}
\alpha \\
\beta
\end{pmatrix}
= \alpha \ket{0} + \beta \ket{1}
.%
\end{align*}
The overall state of a composite quantum system is described using
the \emph{tensor product}, denoted as $\otimes$
\cite[Sec.~2.2.8]{nielsen_quantum_2010}.
@@ -869,9 +899,7 @@ Take for example the two qubits
\ket{\psi_2} = \alpha_2 \ket{0} + \beta_2 \ket{1}
.%
\end{align*}
% TODO: Fix the fact that \psi is used above for the single-qubit
% case and below for the multi-qubit case
We examine the state $\ket{\psi}$ of the composite system as.
We examine the state $\ket{\psi}$ of the composite system.
Assuming the qubits are independent, this is a \emph{product state}
$\ket{\psi} = \ket{\psi_1}\otimes\ket{\psi_2}$.
When not ambiguous, we may omit the tensor product symbol or even write
@@ -893,6 +921,12 @@ We have
\end{align}
We call $\ket{x_0, \ldots, x_n}~, x_i \in \{0,1\}$ the
\emph{computational basis states} \cite[Sec.~4.6]{nielsen_quantum_2010}.
To additionally simplify set notation, we define
\begin{align*}
\mathcal{M}^{\otimes n} := \underbrace{\mathcal{M}\otimes \ldots
\otimes \mathcal{M}}_{n \text{ times}}
.%
\end{align*}
% Entanglement
@@ -902,9 +936,9 @@ An example of such states are the \emph{Bell states}
\begin{align*}
\begin{split}
\ket{\psi_{00}} &= \frac{\ket{00} + \ket{11}}{\sqrt{2}} \hspace{15mm}
\ket{\psi_{01}} = \frac{\ket{01} - \ket{10}}{\sqrt{2}} \\
\ket{\psi_{10}} &= \frac{\ket{00} + \ket{11}}{\sqrt{2}} \hspace{15mm}
\ket{\psi_{11}} = \frac{\ket{01} - \ket{10}}{\sqrt{2}}
\ket{\psi_{01}} = \frac{\ket{01} + \ket{10}}{\sqrt{2}} \\
\ket{\psi_{10}} &= \frac{\ket{01} - \ket{10}}{\sqrt{2}} \hspace{15mm}
\ket{\psi_{11}} = \frac{\ket{00} - \ket{11}}{\sqrt{2}}
\end{split}
\hspace{4mm}.%
\end{align*}
@@ -933,10 +967,10 @@ After examining the modelling of single- and multi-qubit systems,
we now shift our focus to describing the evolution of their states.
We model state changes as operators.
Unlike classical systems, where there are only two possible states and
thus the only possible state change is a bit-flip, a gerenal qubit
thus the only possible state change is a bit-flip, a general qubit
state as shown in \autoref{eq:gen_qubit_state} lives on a continuum of values.
We thus technically also have an infinite number of possible state changes.
Luckily, we can express any operator as a linear combination of the
Fortunately, we can express any operator as a linear combination of the
\emph{Pauli operators} \cite[Sec.~2.2]{gottesman_stabilizer_1997}
\cite[Sec.~2.2]{roffe_quantum_2019}
\begin{align*}
@@ -957,23 +991,31 @@ Luckily, we can express any operator as a linear combination of the
\begin{array}{c}
Z\text{ Operator} \\
\hline\\
\ket{0} \mapsto -\ket{0} \\
\ket{0} \mapsto \phantom{-}\ket{0} \\
\ket{1} \mapsto -\ket{1}
\end{array}%
\hspace{10mm}%
\begin{array}{c}
Y\text{ Operator} \\
\hline\\
\ket{0} \mapsto -j\ket{1} \\
\ket{0} \mapsto \phantom{-}j\ket{1} \\
\hspace{2.75mm}\ket{1} \mapsto -j\ket{0} \hspace*{1mm}.
\end{array}
\end{align*}
In fact, if we allow for complex coefficients, the $X$ and $Z$
operators are sufficient to express any other operator as a linear
combination \cite[Sec.~2.2]{roffe_quantum_2019}.
$I$ is the identity operator and $X$ and $Z$ are referred to as
\emph{bit-flips} and \emph{phase-flips} respectively.
We also call these operators \emph{gates}.
We call the set $\mathcal{G}_n = \left\{ \pm I,\pm jI, \pm X,\pm jX,
\pm Y,\pm jY, \pm Z, \pm jZ \right\}^{\otimes n}$ the \emph{Pauli
group} over $n$ qubits.
In the context of modifying qubit states, we also call operators \emph{gates}.
When working with multi-qubit systems, we can also apply Pauli gates
to individual qubits independently, e.g., $I_1 X_2 I_3 Z_4 Y_5$.
We often omit the identity operators, instead writing $X_2 Z_4 Y_5$.
to individual qubits independently, which we write, e.g., as $I_1 X_2
I_3 Z_4 Y_5$.
We often omit the identity operators, instead writing, e.g., $X_2 Z_4 Y_5$.
Other important operators include the \emph{Hadamard} and
\emph{controlled-NOT (CNOT)} gates \cite[Sec.~1.3]{nielsen_quantum_2010}
\vspace*{-7mm}
@@ -1010,20 +1052,51 @@ Other important operators include the \emph{Hadamard} and
\noindent Many more operators relevant to quantum computing exist, but they are
not covered here as they are not central to this work.
\indent\red{[We only need to consider X and Z errors]
\cite[Equation~8]{roffe_quantum_2019}} \\
\indent\red{[Explain commuting/anticommuting property of operators]
[Journal~p.~46]}
%%%%%%%%%%%%%%%%
\subsection{Quantum Circuits}
\label{Quantum Circuits}
\noindent\indent\red{[Controlled operations]
\cite[Sec.~4.3]{nielsen_quantum_2010}} \\
\indent\red{[In case this reference is needed: Measurements
\cite[Sec.~4.4]{nielsen_quantum_2010}]} \\
\indent\red{[General circuit stuff] \cite[Sec.~1.3.4]{nielsen_quantum_2010}}
% Intro
Using these quantum gates, we can construct \emph{circuits} to manipulate
the states of qubits \cite[Sec.~1.3.4]{nielsen_quantum_2010}.
Circuits are read from left to right and each horizontal wire
represents a qubit whose state evolves as it passes through
successive gates.
% General notation
A single line carries a quantum state, while a double line
denotes a classical bit, typically used to carry the result of a measurement.
A measurement is represented by a meter symbol.
In general, gates are represented as labeled boxes placed on one or more wires.
An exception is the CNOT gate, where the operation is represented as
the symbol $\oplus$.
% Controlled gates & example
We can additionally add a control input to a gate.
This conditions its application on the state of another qubit
\cite[Sec.~4.3]{nielsen_quantum_2010}.
The control connection is represented by a vertical line connecting
the gate to the corresponding qubit, where a filled dot is placed.
A controlled gate applies the respective operation only if the
control qubit is in state $\ket{1}$.
An example of this is the CNOT gate introduced in
\autoref{subsec:Qubits and Multi-Qubit States}, which is depicted in
\autoref{fig:cnot_circuit}.
\begin{figure}[t]
\centering
\begin{quantikz}
\lstick{$\ket{\psi}_1$} & \ctrl{1} & \\
\lstick{$\ket{\psi}_2$} & \targ{} & \\
\end{quantikz}
\caption{CNOT gate circuit.}
\label{fig:cnot_circuit}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Quantum Error Correction}
@@ -1035,11 +1108,11 @@ not covered here as they are not central to this work.
One of the major barriers on the road to building a functioning
quantum computer is the inevitability of errors during quantum
computation due to the difficulty in sufficiently isolating the
computation. These arise due to the difficulty in sufficiently isolating the
qubits from external noise \cite[Intro.]{roffe_quantum_2019}.
This isolation is critical for quantum systems, as the constant interactions
with the environment act as small measurements, leading to the
eventual \emph{decoherence} of the quantum state
with the environment act as small measurements, an effect called
\emph{decoherence} of the quantum state
\cite[Intro.]{gottesman_stabilizer_1997}.
\ac{qec} is one approach of dealing with this problem, by protecting
the quantum state in a similar fashion to information in classical error
@@ -1047,12 +1120,12 @@ correction.
% The unique challenges of QEC
The problem setting of \ac{qec} differs slightly from the classical case, as
three main restrictions apply \cite[Sec.~2.4]{roffe_quantum_2019}:
The problem setting of \ac{qec} differs slightly from the classical case.
Three main restrictions apply \cite[Sec.~2.4]{roffe_quantum_2019}:
\begin{itemize}
\item The no-cloning theorem states that it is
impossible to exactly copy the state of one qubit into another.
\item Qubit are susceptible to more types of errors than
\item Qubits are susceptible to more types of errors than
just bit-flips, as we saw in
\autoref{subsec:Qubits and Multi-Qubit States}.
\item Directly measuring the state of a qubit collapses it onto
@@ -1063,16 +1136,32 @@ three main restrictions apply \cite[Sec.~2.4]{roffe_quantum_2019}:
% General idea (logical vs. physical gates) + notation
Much like in classical error correction, in \ac{qec} information
is protected by mapping it onto codewords in an expanded space,
is protected by mapping it onto codewords in a higher-dimensional space,
thereby introducing redundancy.
To this end, $k \in \mathbb{N}$ \emph{logical qubits} are mapped onto
$n \in \mathbb{N},~n>k$ \emph{physical qubits}.
We circumvent the no-cloning restriction by not copying the state of
the $k$ logical qubits, instead spreading it out over all $n$
physical ones \cite[Intro.]{calderbank_good_1996}
To differentiate a quantum codes from classical ones, we denote a
$n \in \mathbb{N}$ \emph{physical qubits}, $n>k$.
We circumvent the no-cloning restriction by not copying the state of any of
the $k$ logical qubits, instead spreading the total state out over all $n$
physical qubits \cite[Intro.]{calderbank_good_1996}.
To differentiate quantum codes from classical ones, we denote a
code with parameters $k,n$ and minimum distance $d_\text{min}$ using
double brackets, as $[[ n,k,d_\text{min} ]]$ \cite[Sec.~4]{roffe_quantum_2019}.
double brackets, as $\llbracket n,k,d_\text{min} \rrbracket$
\cite[Sec.~4]{roffe_quantum_2019}.
% The backlog problem
Another difference between quantum and classical error correction
lies in the resource constraints.
For \ac{qec}, the most important property is low latency, not, e.g.,
low overall computational complexity.
This is due to the \emph{backlog problem}
\cite[Sec.~II.G.3.]{terhal_quantum_2015}: There are certain gates
at which the effect of existing errors on single qubits may be
exacerbated by transforming them to multi-qubit errors.
We wish to correct the errors before passing qubits through such gates.
If the \ac{qec} system is not fast enough, there will be an increasing
backlog of information at this point in the circuit, leading to an
exponential slowdown in computation.
%%%%%%%%%%%%%%%%
\subsection{Stabilizer Measurements}
@@ -1092,8 +1181,8 @@ Consider the two-qubit repetition code
\underbrace{\ket{11}}_{=:\ket{1}_\text{L}}
.%
\end{align*}
We call $\ket{\psi}_L$ the logical state.
We define the \emph{codespace} as $\mathcal{C} := \text{span}\mleft\{
We call $\ket{\psi}_L$ the logical state, and
we define the \emph{codespace} as $\mathcal{C} := \text{span}\mleft\{
\ket{00}, \ket{11} \mright\}$ and the \emph{error subspace} as
$\mathcal{F} := \text{span} \mleft\{\ket{01}, \ket{10} \mright\}$.
Note that this code is only able to detect single $X$-type errors.
@@ -1132,9 +1221,9 @@ $E\ket{\psi}_\text{L}$ lies in $\mathcal{C}$ or $\mathcal{F}$.
% non-compromising meausrement of the information
To do this without directly observing (and thus potentially
collapsing) the logical state $\ket{\psi}_\text{L}$, we prepare an
ancilla qubit with state $\ket{0}_\text{A}$ and we entangle it with
ancilla qubit with state $\ket{0}_\text{A}$ and entangle it with
$\ket{\psi}_\text{L}$ in such a way that the eigenvalue is indicated
by measuring that instead.
by measuring the ancilla qubit instead.
More specifically, using a stabilizer measurement circuit as shown in
\autoref{fig:stabilizer_measurement}, we transform the state of the
three-qubit system as
@@ -1158,62 +1247,13 @@ the ancilla qubit. Similarly, if $E \ket{\psi}_\text{L} \in
\begin{figure}[t]
\centering
\tikzset{
meter/.append style={
draw, rectangle,
font=\vphantom{A}, minimum width=8mm, minimum height=8mm,
path picture={
\draw[black]
([shift={(.1,.3)}]path picture bounding box.south west)
to[bend left=50]
([shift={(-.1,.3)}]path picture bounding box.south east);
\draw[black,-latex]
([shift={(0,.1)}]path picture bounding box.south)
-- ([shift={(.3,-.1)}]path picture bounding box.north);
}
}
}
\begin{tikzpicture}
\node[rectangle, minimum width=2cm, minimum height=3cm, draw]
(ZZ) {$Z_1Z_2$};
\coordinate (qi1) at (-3, 1);
\coordinate (qi2) at (-3, -1);
\coordinate (qi3) at (-3, -3);
\coordinate (qo1) at (4, 1);
\coordinate (qo2) at (4, -1);
\coordinate (qo3) at (4, -3);
\node[rectangle, minimum width=8mm, minimum height=8mm, draw]
(H1) at ($(qo3 -| ZZ) + (-2, 0)$) {H};
\node[rectangle, minimum width=8mm, minimum height=8mm, draw]
(H2) at ($(qo3 -| ZZ) + (2, 0)$) {H};
\node[circle, fill] (not) at (H1 -| ZZ) {};
\node[meter, right=5mm of H2] (mes) {};
\draw (qi1) -- (ZZ.west |- qi1);
\draw (qi2) -- (ZZ.west |- qi2);
\draw (qo1) -- (ZZ.east |- qo1);
\draw (qo2) -- (ZZ.east |- qo2);
\draw (qi3) -- (H1) -- (not) -- (H2) -- (mes);
\draw (not) -- (ZZ);
\coordinate (qo3u) at ($(qo3) + (0, .5mm)$);
\coordinate (qo3d) at ($(qo3) + (0, -.5mm)$);
\draw (mes.east |- qo3u) -- (qo3u);
\draw (mes.east |- qo3d) -- (qo3d);
\node[left] at (qi3) {$\ket{0}_\text{A}$};
\node[left] at ($(qi1)!.5!(qi2)$) {$E\ket{\psi}_\text{L}$};
\node[right] at ($(qo1)!.5!(qo2)$) {$E\ket{\psi}_\text{L}$};
\end{tikzpicture}
% tex-fmt: off
\begin{quantikz}
\lstick[2]{$E\ket{\psi}_\text{L}$} & & \gate[2]{Z_1Z_2} & & & \\
& & & & & \\
\lstick{$\ket{0}_\text{A}$} & \gate{H} & \ctrl{-1} & \gate{H} & \meter{} & \setwiretype{c} \\
\end{quantikz}
% tex-fmt: on
\caption{Stabilizer measurement circuit for the two-qubit repetition code.}
\label{fig:stabilizer_measurement}
@@ -1237,23 +1277,25 @@ E.g., $P_\mathcal{C}$ will eliminate all components of $E
\ket{\psi}_\text{L}$ that lie in $\mathcal{F}$.
This process, together with the fact that any coherent error can be
decomposed into a linear combination of $X$ and $Z$ errors, means
that it is enough for a \ac{qec} to be able to correct only $X$ and $Z$ errors.
that it is sufficient for \ac{qec} to be able to correct only these
types of errors.
This effect is referred to as error \emph{digitization}
\cite[Sec.~2.2]{roffe_quantum_2019}.
% The stabilizer group
Operators such as $Z_1Z_2$ above are called \emph{stabilizers}.
An operator $P_i \in \mathcal{G}_n$ is called a stabilizer of an
More generally, an operator $P_i \in \mathcal{G}_n$ is called a stabilizer of an
$[[n, k, d_\text{min}]]$ code $\mathcal{C}$, if
\begin{itemize}
\item It stabilizes all logical states, i.e.,
$P_i\ket{\psi}_\text{L} = (+1)\ket{\psi}_\text{L} ~\forall~
\ket{\psi}_\text{L} \in \mathcal{C}$.
\item It commutes with all other stabilizers of the code. This
property is important to be able to measure the eigenvalue of
a stabilizer without disturbing the eigenvectors of the
others \cite[Sec.~1.2]{gottesman_stabilizer_1997}.
\item It commutes with all other stabilizers $P_j$ of the code,
i.e., $[P_i, P_j] = 0$.
This property is important to be able to measure the
eigenvalue of a stabilizer without disturbing the
eigenvectors of the others \cite[Sec.~1.2]{gottesman_stabilizer_1997}.
\end{itemize}
Formally, we define the \emph{stabilizer group} $\mathcal{S}$ as
\cite[Sec.~4.1]{roffe_quantum_2019}
@@ -1291,11 +1333,11 @@ and the stabilizer measurement returns 1.
For classical binary linear block codes, we use $n-k$ parity-checks
to reduce the degrees of freedom introduced by the encoding operation.
Effectively, each parity-check defines a local code, splitting the
vector space in half, with only one half containing valid codewords.
Effectively, each parity-check defines a local code splitting the
vector space in half, with only one part containing valid codewords.
The global code is the intersection of all local codes.
We can do the same in the quantum case.
Each split is represented using stabilizer, whose eigenvalues signify
Each split is represented using a stabilizer, whose eigenvalues signify
whether a candidate vector lies in the local codespace or local error subspace.
It is only a valid codeword if it lies in the codespace of all local codes.
We call codes constructed this way \emph{stabilizer codes}.
@@ -1305,45 +1347,86 @@ We call codes constructed this way \emph{stabilizer codes}.
Similar to the classical case, we can use a syndrome vector to
describe which local codes are violated.
To obtain the syndrome, we simply measure the corresponding
operators, each using a circuit as explained in
operators $P_i$, each using a circuit as explained in
\autoref{subsec:Stabilizer Measurements}.
A full \emph{syndrome extraction circuit} is depicted in \autoref{fig:sec}.
Note that this is an abstract representation of the syndrome extraction.
For the actual implementation in hardware, we can transform this into
a circuit that requires only CNOT and H-gates
\cite[Sec.~10.5.8]{nielsen_quantum_2010}.
% TODO: Move this further up to the commutativity of operators?
\indent\red{[Fixing the error after finding it
\cite[Sec.~10.5.5]{nielsen_quantum_2010}]} \\
\indent\red{[Logical operators \cite[Sec.~4.2]{roffe_quantum_2019}]} \\
\indent\red{[Measuring logical operators gives yields the outcomes of
the encoded computations \cite[Sec.~2.6]{derks_designing_2025}]} \\
\indent\red{[X and Z measurements can be performed with only CNOT and
Hadamard gates \cite[Sec.~10.5.8]{nielsen_quantum_2010}]} \\
\indent\red{[(?) Stabilizer generators]} \\
\indent\red{[Parity-check matrix \cite[Sec.~10.5.1]{nielsen_quantum_2010}]}
% Logical operators
In order to modify the logical state encoded using the physical
qubits, we can use \emph{logical operators} \cite[Sec.~4.2]{roffe_quantum_2019}.
For each qubit, there are two logical operators, $X_i$ and $Z_j$.
These are operators that
\begin{itemize}
\item Commute with all the stabilizers in $\mathcal{S}$.
\item Anti-commute with one another, i.e., $[ \overline{X}_i,
\overline{Z}_i ]_{+} = \overline{X}_i \overline{Z}_i +
\overline{Z}_i \overline{X}_i = 0$.
\end{itemize}
We can also measure these operators to find out the logical state a
physical state corresponds to \cite[Sec.~2.6]{derks_designing_2025}.
% Parity-check matrix
% TODO: Do I have to introduce before that stabilizers only need X
% and Z operators?
We can represent stabilizer codes using a \emph{check matrix}
\cite[Sec.~10.5.1]{nielsen_quantum_2010}
\begin{align*}
\bm{H} = \left[
\begin{array}{c|c}
\bm{H}_X & \bm{H}_Z
\end{array}
\right]
,%
\end{align*}
with $\bm{H} \in \mathbb{F}_2^{(n-k)\times(2n)}$.
This is similar to a classical \ac{pcm} in that it contains $n-k$
rows, each describing one constraint. Each constraint restricts an additional
degree of freedom of the higher-dimensional space we use to introduce
redundancy.
In contrast to the classical case, this matrix now has $2n$ columns,
as we have to consider both the $X$ and $Z$ type operators that make up
the stabilizers.
Take for example the Steane code \cite[Eq.~10.83]{nielsen_quantum_2010}.
We can describe it using the check matrix
\begin{align}
\label{eq:steane}
\bm{H}_\text{Steane} = \left[
\begin{array}{ccccccc|ccccccc}
0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1
\end{array}
\right]
.%
\end{align}
The first $n$ columns correspond to $X$ operators acting on the
corresponding physical qubit, the rest to the $Z$ operators.
\begin{figure}[t]
\centering
\tikzset{
meter/.append style={
draw, rectangle,
font=\vphantom{A}, minimum width=8mm, minimum height=8mm,
path picture={
\draw[black]
([shift={(.1,.3)}]path picture bounding box.south west)
to[bend left=50]
([shift={(-.1,.3)}]path picture bounding box.south east);
\draw[black,-latex]
([shift={(0,.1)}]path picture bounding box.south)
-- ([shift={(.3,-.1)}]path picture bounding box.north);
}
}
}
\red{Hier könnte Ihre Werbung stehen.}
% tex-fmt: off
\begin{quantikz}
\lstick[2]{$E\ket{\psi}_\text{L}$} & & \gate[2]{P_1} & \gate[2]{P_2} & \gate[style={draw=none},2]{\ldots} & \gate[2]{P_{n-k}} & & & \\
& & & & & & & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & \gate{H} & \ctrl{-1} & & & & \gate{H} & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & \gate{H} & & \ctrl{-2} & & & \gate{H} & \meter{} & \setwiretype{c} \\
\vdots\setwiretype{n} & & & & & & & & \vdots \\
\lstick{$\ket{0}_{\text{A}_{n-k}}$} & \gate{H} & & & & \ctrl{-4} & \gate{H} & \meter{} & \setwiretype{c} \\
\end{quantikz}
% tex-fmt: on
\caption{
\red{Illustration of a general syndrome extraction circuit.
Adapted from \cite[Figure~4]{roffe_quantum_2019}.}
Illustration of a full syndrome extraction circuit.
Adapted from \cite[Figure~4]{roffe_quantum_2019}.
}
\label{fig:sec}
\end{figure}
@@ -1352,76 +1435,275 @@ Hadamard gates \cite[Sec.~10.5.8]{nielsen_quantum_2010}]} \\
\subsection{Calderbank-Shor-Steane Codes}
\label{subsec:Calderbank-Shor-Steane Codes}
% Intro
Stabilizer codes are especially practical to work with when they can
handle $X$- and $Z$-type errors independently.
We can then separate the stabilizer generators into some with only
$Z$ operators and some with only $X$ operators.
handle $X$ and $Z$ type errors independently.
As $Z$ errors anti-commute with $X$ operators in the stabilizers and
vice versa, this property translates into being able to split the
stabilizers into a subset being made up of only $X$
operators and the rest only of $Z$ operators.
We call such codes \ac{css} codes.
We can see this property in \autoref{eq:steane} in the check matrix
of the Steane code.
\indent\red{[Z-type operators for X type errors and vice versa ]} \\
\indent\red{[Construction from two binary linear codes
\cite[p.~452,469]{nielsen_quantum_2010}]}
% Construction
We can exploit this separate consideration of $X$ and $Z$ errors in
the construction of \ac{css} codes.
We combine two binary linear codes $\mathcal{C}_1$ and
$\mathcal{C}_2$, each responsible for correcting one type of error
\cite[Sec.~10.5.6]{nielsen_quantum_2010}.
Using the dual code of $\mathcal{C}_2$ \cite[Eq.~3.4]{ryan_channel_2009}
\begin{align*}
\mathcal{C}_2^\perp := \left\{ \bm{x}' \in \mathbb{F}^2 :
\bm{x}' \bm{x}^\text{T} = 0 ~\forall \bm{x} \in \mathcal{C}_2 \right\}
,%
\end{align*}
we define $\bm{H}_X := \bm{H}(\mathcal{C}_2^\perp)$ and $\bm{H}_Z
:= \bm{H}(\mathcal{C}_1)$, and construct the check matrix as
\begin{align*}
\left[
\begin{array}{c|c}
\bm{H}_X & \bm{0} \\
\bm{0} & \bm{H}_Z
\end{array}
\right]
.%
\end{align*}
In order to yield a valid stabilizer code, $\mathcal{C}_1$ and
$\mathcal{C}_2$ must satisfy the commutativity condition
\begin{align}
\label{eq:css_condition}
\bm{H}_X \bm{H}_Z^\text{T} = \bm{0}
.%
\end{align}
We can ensure this is the case by choosing them such that
$\mathcal{C}_2 \subset \mathcal{C}_1$.
%%%%%%%%%%%%%%%%
\subsection{Quantum Low-Density Parity-Check Codes}
\label{subsec:Quantum Low-Density Parity-Check Codes}
% Intro
Various methods of constructing \ac{qec} codes exist
\cite{swierkowska_eccentric_2025}.
Topological codes, for example, encode information in the features of
a lattice and are intrinsically robust against local errors.
Among these, the \emph{surface code} is the most widely studied.
Another example are concatenated codes, which nest one code within
another, allowing for especially simple and flexible constructions
\cite[Sec.~3.2]{swierkowska_eccentric_2025}.
An area of research that has recently seen more attention is that of
quantum \ac{ldpc} (\acs{qldpc}) codes.
They have much better encoding efficiency than, e.g., the surface
code, scaling up of which would be prohibitively expensive
\cite[Sec.~I]{bravyi_high-threshold_2024}.
% Bivariate Bicycle codes
A recent addition to the class of \ac{qldpc} codes is that of \ac{bb}
codes \cite[Sec.~3]{bravyi_high-threshold_2024}.
These are a special type of \ac{css} code, where $\bm{H}_X$ and
$\bm{H}_Z$ are constructed from two matrices $\bm{A}$ and $\bm{B}$ as
\begin{align*}
\bm{H}_X = [\bm{A} \vert \bm{B}]
\hspace*{5mm} \text{and} \hspace*{5mm}
\bm{H}_Z = [\bm{B}^\text{T} \vert \bm{A}^\text{T}]
.%
\end{align*}
This way, we can guarantee the satisfaction of the commutativity
condition (\autoref{eq:css_condition}).
To define $\bm{A}$ and $\bm{B}$ we first introduce some additional notation.
We denote the identity matrix as $\bm{I_l} \in \mathbb{F}^{l\times l}$ and
the \emph{cyclic shift matrix} as $\bm{S_l} \in \mathbb{F}^{l\times
l},~S_{l,i,j}= \delta_{i+1,j}$, with $l \in \mathbb{N}$.
We further define
\begin{align*}
x = \bm{S}_l \otimes \bm{I}_m
\hspace*{5mm} \text{and} \hspace*{5mm}
y = \bm{I}_l \otimes \bm{S}_m
.%
\end{align*}
We can then construct $\bm{A}$ and $\bm{B}$ as bivariate polynomials
\begin{align*}
\bm{A} = \bm{A}_1 + \bm{A}_2 + \bm{A}_3
\hspace*{5mm} \text{and} \hspace*{5mm}
\bm{B} = \bm{B}_1 + \bm{B}_2 + \bm{B}_3
,%
\end{align*}
where $\bm{A}_i$ and $\bm{B}_i$ are powers of $\bm{x}$ or $\bm{y}$.
\ac{bb} codes have large minimum distance $d_\text{min}$ and high rate,
offering a more than 10-fold reduction of encoding overhead over the
surface code.
Additionally, they posess short-depth syndrome measurement circuits,
leading to lower time requirements for the syndrome extraction
and thus lower error rates \cite[Sec.~1]{bravyi_high-threshold_2024}.
% Syndrome-based BP
As we saw in \autoref{subsec:Stabilizer Measurements}, we work only
with the parity information contained in the syndrome, to avoid
disturbing the quantum states of individual qubits.
This necessitates a modification of the standard \ac{bp} algorithm
introduced in \autoref{subsec:Iterative Decoding}
\cite[Sec.~3.1]{yao_belief_2024}.
Instead of attempting to find the most likely codeword directly, the
algorithm will now try to find an error pattern $\hat{\bm{e}} \in
\mathbb{F}_2^n$ that satisfies
\begin{align*}
\bm{H} \hat{\bm{e}}^\text{T} = \bm{s}
.%
\end{align*}
To this end, we initialize the channel \acp{llr} as
\begin{align*}
\tilde{L}_i = \log{\frac{P(X_i = 0)}{P(X_i = 1)}} = \log{\frac{1
- p_i}{p_i}}
,%
\end{align*}
where $p_i$ is the prior probability of error of \ac{vn} $i$.
Additionally, we amend the \ac{cn} update to consider the parity
indicated by the syndrome, calculating
\begin{align*}
L_{i\leftarrow j} = 2\cdot (-1)^{s_j} \cdot \tanh^{-1} \left( \prod_{i'\in
\mathcal{N}(j)\setminus \{i\}} \tanh \frac{L_{i'\rightarrow j}}{2} \right)
.
\end{align*}
The resulting syndrome-based \ac{bp} algorithm is shown in
algorithm \ref{alg:syndome_bp}.
\noindent\red{[Constant overhead scaling]} \\
\noindent\red{[Scaling of minimum distance with code length]} \\
\noindent\red{[Bivariate Bicycle codes]} \\
\noindent\red{[Decoding QLDPC codes (syndrome-based BP)]} \\
\noindent\red{[Degeneracy -> BP+OSD, BPGD]} \\
\noindent\red{[``The task of decoding is therefore to infer, from a
measured syndrome, the most likely error coset rather than the exact
physical error.''
% tex-fmt: off
\cite[Sec.~II~B)]{koutsioumpas_colour_2025}]}%
\tikzexternaldisable
\begin{algorithm}[t]
\caption{Binary syndrome-based belief propagation (BP) algorithm.}
\label{alg:syndome_bp}
\begin{algorithmic}[1]
\State \textbf{Initialize:} $\tilde{L}_i \leftarrow
\log \frac{1-p_i}{p_i}$ for all $i \in \mathcal{I}$
\State \textbf{Initialize:} $L_{i \rightarrow j} \leftarrow
\tilde{L}_i$ for all $i \in \mathcal{I},\, j \in \mathcal{N}_\text{V}(i)$
\State \textbf{Initialize:} $\hat{e} \leftarrow \bm{0}$
\For{$\ell = 1, \ldots, n_\text{iter}$}
\For{$j \in \mathcal{J}$}
\For{$i \in \mathcal{N}_\text{C}(j)$}
\State $\displaystyle L_{i \leftarrow j} \leftarrow
2\cdot(-1)^{s_j}\cdot\tanh^{-1}
\!\left(
\prod_{i' \in \mathcal{N}_\text{C}(j)\setminus\{i\}}
\tanh\frac{L_{i'\rightarrow j}}{2}
\right)$
\EndFor
\EndFor
\For{$i \in \mathcal{I}$}
\For{$j \in \mathcal{N}_\text{V}(i)$}
\State $\displaystyle L_{i \rightarrow j} \leftarrow
\tilde{L}_i +
\sum_{j' \in \mathcal{N}_\text{V}(i)\setminus\{j\}}
L_{i \leftarrow j'}$
\EndFor
\EndFor
\For{$i \in \mathcal{I}$}
\State $\displaystyle \hat{e}_i \leftarrow
\mathbbm{1}\left\{
\tilde{L}_i +
\sum_{j \in \mathcal{N}_\text{V}(i)} L_{i \leftarrow j} < 0
\right\}$
\EndFor
\If{$\bm{H}\hat{\bm{e}}^\text{T} = \bm{s}$}
\State \textbf{break}
\EndIf
\EndFor
\State \textbf{return} $\hat{\bm{e}}$
\end{algorithmic}
\end{algorithm}
\tikzexternalenable
% tex-fmt: on
\\
\red{
\textbf{General Notes:}
\begin{itemize}
\item Note that there are other codes than stabilizer codes
(and research and give some examples), but only
stabilizer codes are considered in this work
\item Degeneracy
\item The QEC decoding problem (considering degeneracy)
\cite[Sec.~2.3]{yao_belief_2024}
\end{itemize}
\textbf{Content:}
\begin{itemize}
\item General context
\begin{itemize}
\item Why we need QEC (correcting errors due
to noisy gates)
\item Main challenges of QEC compared to classical
error correction
\item Logical vs physical states, logical vs
physical operators
\end{itemize}
\item Stabilizer codes
\begin{itemize}
\item Definition of a stabilizer code
\item The stabilizer its generators (note somewhere
that the generators have to commute
to be able to
be measured without disturbing each other)
(Why we need commutativity of the
stabilizers [Journal,
p.~51], [Got97, p.~6])
\item syndrome extraction circuit
\item Stabilizer codes are effectively the QM
% TODO: Actually binary linear codes or
% just linear codes?
equivalent of binary linear codes (e.g.,
expressible via check matrix)
\item Similar to parity checks, quantum states can be
more conveniently described using stabilizers
rather than working with the states directly
\cite[Sec.~10.5.1]{nielsen_quantum_2010}
\end{itemize}
\item Digitization of errors
\item CSS codes
\item Color codes?
\item Surface codes?
\end{itemize}
}
% Degeneracy and short cycles
Decoding \ac{qldpc} codes poses some unique challenges.
One issue is that of \emph{quantum degeneracy}.
Because errors that differ by a stabilizer have the same impact on
all codewords, there can be multiple minimum-weight solutions to the
quantum decoding problem \cite[Sec.~II.C.]{babar_fifteen_2015}
\cite[Sec.~V]{roffe_decoding_2020}.
This leads to the decoding algorithm getting confused about the
direction to proceed in \cite[Sec.~5]{yao_belief_2024}.
Another problem is that due to the commutativity property of the stabilizers,
quantum codes inherently contain short cycles
\cite[Sec.~IV.C]{babar_fifteen_2015}.
As discussed in \autoref{subsec:Iterative Decoding}, these lead to
the violation of the independence assumption of the messages passed
during decoding, impeding performance.
% BPGD
The aforementioned issues both manifest themselves as convergence problems
of the \ac{bp} algorithm, and different ways of modifying the algorithm
to aid with convergence exist.
One approach is to use \ac{bp} with guided decimation (\acs{bpgd})
\cite[Alg.~1]{yao_belief_2024}.
Here, a number $T\in \mathbb{N}$ of \ac{bp} iterations are performed,
before \emph{decimating} the most reliable \ac{vn}, i.e., performing
a hard decision and excluding it from further decoding.
This constrains the solution space more and more as the decoding
progresses, encouraging the algorithm to converge to one of the
solutions \cite[Sec.~5]{yao_belief_2024}.
Algorithm \ref{alg:bpgd} shows this process.
Note that as the Tanner graph only has $n$ \acp{vn}, this is a
natural constraint on the maximum number of outer iterations of the algorithm.
% TODO: Explain that setting the channel LLR to infinity is the same
% as a hard decision and ignoring the VN in the further decoding
% tex-fmt: off
\tikzexternaldisable
\begin{algorithm}[t]
\caption{Belief propagation with guided decimation (BPGD) algorithm.}
\label{alg:bpgd}
\begin{algorithmic}[1]
\State \textbf{Initialize:} $\tilde{L}_i \leftarrow
\log \frac{1-p_i}{p_i}$ for all $i \in \mathcal{I}$
\State \textbf{Initialize:} $L_{i \rightarrow j} \leftarrow
\tilde{L}_i$ for all $i \in \mathcal{I},\, j \in \mathcal{N}_\text{V}(i)$
\State \textbf{Initialize:} $\hat{e} \leftarrow \bm{0}$
\State \textbf{Initialize:} $\mathcal{I}' \leftarrow \mathcal{I}$
\For{$r = 1, \ldots, n$}
\For{$\ell = 1, \ldots, T$}
\State Perform \ac{cn} update
\State Perform \ac{vn} update
\State $L^\text{total}_i \leftarrow \tilde{L}_i + \sum_{j \in \mathcal{N}_\text{V}(i)} L_{i \leftarrow j}$
\EndFor
\For{$i \in \mathcal{I}$}
\State $\displaystyle \hat{e}_i \leftarrow
\mathbbm{1}\left\{ L^\text{total}_i \right\}$
\EndFor
\If{$\bm{H}\hat{\bm{e}}^\text{T} = \bm{s}$}
\State \textbf{break}
\Else
\State $i_\text{max} \leftarrow \argmax_{i \in \mathcal{I}'} \lvert L^\text{total}_i \rvert $
\If{$L^\text{total}_{i_\text{max}} < 0$}
\State $\tilde{L}_{i_\text{max}} \leftarrow -\infty$
\Else
\State $\tilde{L}_{i_\text{max}} \leftarrow +\infty$
\EndIf
\State $\mathcal{I}' \leftarrow \mathcal{I}'\setminus\{i_\text{max}\}$
\EndIf
\EndFor
\State \textbf{return} $\hat{\bm{e}}$
\end{algorithmic}
\end{algorithm}
\tikzexternalenable
% tex-fmt: on

View File

@@ -1,18 +1,643 @@
% TODO: Make all [H] -> [t]
\chapter{Fault Tolerant QEC}
\section{Fault Tolerance}
% Intro
\content{Syndrome extraction circuitry itself introduces errors}
\content{High level explanation of fault tolerance (with figure)}
\content{Mathematical definition of fault tolerance}
% Practical considerations
\content{We generally need to perform multiple rounds of syndrome extraction}
\content{The number of rounds of syndrome extraction is usually
chosen equal to the $d_\text{min}$ of the code}
\content{One-shot decoding property}
\begin{figure}[H]
\centering
\begin{tikzpicture}
\node[rectangle, draw, fill=orange!20, minimum
height=2cm, minimum width=2.5cm, align=left] at (0,0)
(internal) {Internal\\ Errors};
\node[signal, draw, fill=orange!20, minimum height=2cm,
minimum width=2.5cm, align=left, signal pointer angle=140]
at (-2.45, 0) (input) {Input\\ Errors};
\node at (1.97,0) {\huge =};
\node[rectangle, draw, fill=orange!20, minimum height=2cm,
minimum width=2.5cm, align=left] at (4,0) (output)
{Output\\ Errors};
\node[above] at (input.north) {\small Input State};
\node[above] at (internal.north) {\small QEC};
\node[above] at (output.north) {\small Output State};
\end{tikzpicture}
\caption{Sources of error in a fault-tolerant \ac{qec} system.}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Noise Models}
\subsection{Depolarizing Channel}
\subsection{Phenomenological Noise}
\subsection{Circuit-Level Noise}
\label{sec:Noise Models}
% Intro
\content{Explanation of what a noise model is}
\content{Mention there are different types of noise models, each with
different possible error locations}
% Figure intro
\content{\autoref{fig:pure_syndrome_extraction} shows the syndrome
extraction circuit of a three-qubit repetition code with stabilizers
$Z_1Z_2$ and $Z_2Z_3$. This code is only able to deal with X errors.
We will use it as a propotypical model to examine the different types
of noise models}
\content{This is now a concrete implementation of the syndrome
measurement circuit using CNOT gates, as opposed to the system-level
view in \autoref{subsec:Stabilizer Codes}}
\content{\autoref{fig:noise_model_types} shows a number of diffent
types of noise models}
% Bit-flip noise
\content{Bit-flip noise}
\content{Introduce \emph{data qubits}}
\content{Only X errors on data qubits}
\content{Most similar to classical channel coding}
\content{\textbf{TODO}: What is this useful for? Just as a first step?}
% Depolarizing channel
\content{Depolarizing channel}
\content{X/Y/Z errors on data qubits}
\content{\textbf{TODO}: What does this model? Memory experiment with
ideal syndrome extraction?}
\content{\textbf{TODO}: Why is it called depolarizing?}
% Phenomenological noise
\content{Phenomenological noise}
\content{First noise model that considers errors during syndrome extraction}
\content{X errors before each syndrome extraction round}
\content{X errors before measurements}
\content{\textbf{TODO}: Why is this useful? Derks et al. mentioned
something about it being useful to derive fault-tolerant circuits}
\content{\textbf{TODO}: Make sure phenomenological noise is only X errors}
% Circuit-level noise
\content{Circuit-level noise}
\content{This is generally what we strive to be able to decode under}
\content{X/Y/Z errors before each syndrome extraction round}
\content{$n$-qubit Pauli errors after each $n$-qubit Pauli gate}
\content{Define $n$-qubit Pauli errors}
\content{X errors right before the measurements}
\content{Note that the only errors right before the measurements that
have any effect on the measurement outcomes are X errors. That is why
it is enough to consider this type of error at this point in the circuit.}
% Different noise models for circuit-level noise
\content{Comparison from Gidney's paper}
\content{In this work we only consider standard circuit-based
depolarizing noise}
\begin{figure}[H]
\centering
\begin{minipage}{0.5\textwidth}
\begin{align*}
\bm{H} =
\begin{pmatrix}
1 & 1 & 0 \\
0 & 1 & 1
\end{pmatrix}
\end{align*}
\end{minipage}%
\begin{minipage}{0.5\textwidth}
% tex-fmt: off
\begin{quantikz}%[row sep=4mm, column sep=4mm]
\lstick[3]{$\ket{\psi}_\text{L}$} & \ctrl{3} & & & & & \\
& & \ctrl{2} & \ctrl{3} & & & \\
& & & & \ctrl{2} & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & \targ{} & \targ{} & & & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & \targ{} & \targ{} & \meter{} & \setwiretype{c}
\end{quantikz}
% tex-fmt: on
\end{minipage}%
\caption{
Syndrome extraction circuit for the three-qubit repetition
code under bit-flip noise.
}
\label{fig:pure_syndrome_extraction}
\end{figure}
\begin{figure}[H]
\centering
\newcommand{\xerr}{\gate[style={fill=KITblue!50}]{\phantom{1}}}
\newcommand{\xyzerr}{\gate[style={
draw=black,
fill=KITred,
path picture={
% tex-fmt: off
\fill[KITblue!60]
($(path picture bounding box.south west)+(0,0)$)
-- ($(path picture bounding box.north west)+(0,0)$)
-- ($(path picture bounding box.north west)+(0.28,0)$)
-- cycle;
\fill[KITorange!60]
($(path picture bounding box.north east)+(0,0)$)
-- ($(path picture bounding box.south east)+(0,0)$)
-- ($(path picture bounding box.south east)+(-0.28,0)$)
-- cycle;
\fill[KITred!60]
($(path picture bounding box.north east)+(0,0)$)
-- ($(path picture bounding box.south east)+(-0.28,0)$)
-- ($(path picture bounding box.south west)+(0,0)$)
-- ($(path picture bounding box.north west)+(0.28,0)$)
-- cycle;
% tex-fmt: on
}
}]{\phantom{1}}}
\begin{minipage}{0.7\textwidth}
\begin{minipage}{\textwidth}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=4mm]
\lstick[3]{$\ket{\psi}_\text{L}$} & \xerr & \ctrl{3} & & & & & \\
& \xerr & & \ctrl{2} & \ctrl{3} & & & \\
& \xerr & & & & \ctrl{2} & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & & \targ{} & \targ{} & & & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & & \targ{} & \targ{} & \meter{} & \setwiretype{c}
\end{quantikz}
% tex-fmt: on
\subcaption{Bit-flip noise.}
\end{minipage}
\vspace*{5mm}
\begin{minipage}{\textwidth}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=4mm]
\lstick[3]{$\ket{\psi}_\text{L}$} & \xyzerr & \ctrl{3} & & & & & \\
& \xyzerr & & \ctrl{2} & \ctrl{3} & & & \\
& \xyzerr & & & & \ctrl{2} & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & & \targ{} & \targ{} & & & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & & \targ{} & \targ{} & \meter{} & \setwiretype{c}
\end{quantikz}
% tex-fmt: on
\subcaption{Depolarizing channel.}
\end{minipage}
\vspace*{5mm}
\begin{minipage}{\textwidth}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=4mm]
\lstick[3]{$\ket{\psi}_\text{L}$} & \xerr & \ctrl{3} & & & & & & \xerr & & \setwiretype{n} & \\
& \xerr & & \ctrl{2} & \ctrl{3} & & & & \xerr & & \setwiretype{n} & \gate[style={left,draw=none}]{\cdots} \\
& \xerr & & & & \ctrl{2} & & & \xerr & & \setwiretype{n} & \\
\lstick{$\ket{0}_{\text{A}_1}$} & & \targ{} & \targ{} & & & \xerr & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & & \targ{} & \targ{} & \xerr & \meter{} & \setwiretype{c}
\end{quantikz}
% tex-fmt: on
\subcaption{Phenomenological noise.}
\end{minipage}
\vspace*{5mm}
\begin{minipage}{\textwidth}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=2mm]
\lstick[3]{$\ket{\psi}_\text{L}$} & \xyzerr & \ctrl{3} & \xyzerr \wire[d][3]{q} & & & & & & & & & \xyzerr & & \setwiretype{n} & \\
& \xyzerr & & & \ctrl{2} & \xyzerr \wire[d][2]{q} & \ctrl{3} & \xyzerr \wire[d][3]{q} & & & & & \xyzerr & & \setwiretype{n} & \gate[style={left,draw=none,xshift=3.5mm}]{\cdots} \\
& \xyzerr & & & & & & & \ctrl{2} & \xyzerr \wire[d][2]{q} & & & \xyzerr & & \setwiretype{n} & \\
\lstick{$\ket{0}_{\text{A}_1}$} & \xyzerr & \targ{} & \xyzerr & \targ{} & \xyzerr & & & & & \xerr & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & \xyzerr & & & & & \targ{} & \xyzerr & \targ{} & \xyzerr & \xerr & \meter{} & \setwiretype{c}
\end{quantikz}
% tex-fmt: on
\subcaption{Circuit-level noise.}
\end{minipage}
\end{minipage}%
\hfill%
\begin{minipage}{0.23\textwidth}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=2mm]
\setwiretype{n} & \xerr & \gate[style={right, draw=none, xshift=-15mm}]{\text{X error}} \\
\setwiretype{n} & \xyzerr & \gate[style={right, draw=none, xshift=-15mm}]{\text{X,Z, or Y error}} \\
\setwiretype{n} & \gate{\phantom{1}}\wire[d][1]{q} & \gate[style={right, draw=none, xshift=-15mm},2]{\text{Correlated error}} \\
\setwiretype{n} & \gate{\phantom{1}} &
\end{quantikz}
% tex-fmt: on
\end{minipage}
\caption{Types of noise models.}
\label{fig:noise_model_types}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Detector Error Models}
\label{sec:Detector Error Models}
\content{\textbf{TODO}: Look up how Derks et al. introduce DEMs}
% Different ways of implementing fault tolerance
\content{Ways of implementing fault tolerance different from DEMs}
% Core idea
\content{Model additional error locations in the code}
\content{Construct ``circuit code'' from original code}
% Benefits
\content{Benefits of this approach}
%%%%%%%%%%%%%%%%
\subsection{Measurement Syndrome Matrix}
\label{subsec:Measurement Syndrome Matrix}
% Core idea
\content{Core idea: Matrix describes parity checks \\
$\rightarrow$ A column shows which parity checks the
corresponding VN contributes to \\
$\rightarrow$ View columns as syndromes corresponding to error
locations in the circuit
}
% Multiple rounds of syndrome extraction
% TODO: First introduce syndrome measurement matrix, mathematically
% (consult Derks et al.'s paper). Then use the three-qubit repetition
% code as an example only
\autoref{fig:rep_code_multiple_rounds_bit_flip} shows a circuit
performing three rounds of syndrome extraction for the three-qubit
repetition code introduced earlier.
We are only considering bit-flip noise at this point.
For each syndrome extraction round, we get an additional set of
syndrome measurements.
We combine these measurements by stacking them in a new vector $\bm{s}
\in \mathbb{F}_2^{n_\text{rounds}\cdot(n-k)}$.
To model this behavior mathematically, we append additional rows to
the check matrix.
We call this matrix the \emph{measurement syndrome matrix}
$\bm{\Omega}$.
\begin{figure}[H]
\centering
\begin{minipage}{0.3\textwidth}
\centering
\begin{tikzpicture}
\node{$%
\bm{\Omega} =
\begin{pmatrix}
1 & 1 & 0 \\
0 & 1 & 1 \\
1 & 1 & 0 \\
0 & 1 & 1 \\
1 & 1 & 0 \\
0 & 1 & 1
\end{pmatrix}%
$
};
\draw [
line width=1pt,
decorate,
decoration={brace,mirror,amplitude=3mm,raise=5mm}
]
(1,0.55) -- (1,1.4)
node[midway,right,xshift=10mm]{$\text{SE}_1$};
\draw [
line width=1pt,
decorate,
decoration={brace,mirror,amplitude=3mm,raise=5mm}
]
(1,-0.4) -- (1,0.45)
node[midway,right,xshift=10mm]{$\text{SE}_2\hspace{2mm},$};
\draw [
line width=1pt,
decorate,
decoration={brace,mirror,amplitude=3mm,raise=5mm}
]
(1,-1.38) -- (1,-0.5)
node[midway,right,xshift=10mm]{$\text{SE}_3$};
\end{tikzpicture}
\end{minipage}%
\begin{minipage}{0.3\textwidth}
\centering
\vspace*{-6mm}
\begin{gather*}
\bm{s} \in \text{span} \mleft\{ \bm{\Omega} \mright\}
\end{gather*}
\end{minipage}
\newcommand{\preperr}[1]{
\gate[style={fill=blue!20}]{\scriptstyle #1}
}
\vspace*{5mm}
\begin{quantikz}[
row sep=4mm, column sep=4mm,
wire types={q,q,q,q,q,n,n,n,n},
execute at end picture={
\draw [
line width=1pt,
decorate,
decoration={brace,amplitude=3mm,raise=9mm}
]
(\tikzcdmatrixname-4-19.north east)
--
(\tikzcdmatrixname-5-19.south east)
node[midway,right,xshift=14mm]{$\text{SE}_1$};
\draw [
line width=1pt,
decorate,
decoration={brace,amplitude=3mm,raise=9mm}
]
(\tikzcdmatrixname-6-19.north east)
--
(\tikzcdmatrixname-7-19.south east)
node[midway,right,xshift=14mm]{$\text{SE}_2$};
\draw [
line width=1pt,
decorate,
decoration={brace,amplitude=3mm,raise=9mm}
]
(\tikzcdmatrixname-8-19.north east)
--
(\tikzcdmatrixname-9-19.south east)
node[midway,right,xshift=14mm]{$\text{SE}_3$};
}
]
% tex-fmt: off
\lstick[3]{$\ket{\psi}_\text{L}$} & \preperr{E_0} & \ctrl{3} & & & & & & \ctrl{5} & & & & & & \ctrl{7} & & & & & \\
& \preperr{E_1} & & \ctrl{2} & \ctrl{3} & & & & & \ctrl{4} & \ctrl{5} & & & & & \ctrl{6} & \ctrl{7} & & & \\
& \preperr{E_2} & & & & \ctrl{2} & & & & & & \ctrl{4} & & & & & & \ctrl{6} & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & & \targ{} & \targ{} & & & & & & & & & & & & & & & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & & \targ{} & \targ{} & & & & & & & & & & & & & \meter{} & \setwiretype{c} \\
& & & & & & \lstick{$\ket{0}_{\text{A}_3}$} & \setwiretype{q} & \targ{} & \targ{} & & & & & & & & & \meter{} & \setwiretype{c} \\
& & & & & & \lstick{$\ket{0}_{\text{A}_4}$} & \setwiretype{q} & & & \targ{} & \targ{} & & & & & & & \meter{} & \setwiretype{c} \\
& & & & & & & & & & & & \lstick{$\ket{0}_{\text{A}_5}$} & \setwiretype{q} & \targ{} & \targ{} & & & \meter{} & \setwiretype{c} \\
& & & & & & & & & & & & \lstick{$\ket{0}_{\text{A}_6}$} & \setwiretype{q} & & & \targ{} & \targ{} & \meter{} & \setwiretype{c}
% tex-fmt: on
\end{quantikz}
\caption{
Repeated syndrome extraction circuit for the three-qubit
repetition code under bit flip noise.
}
\label{fig:rep_code_multiple_rounds_bit_flip}
\end{figure}
\begin{figure}[H]
\begin{gather*}
\hspace*{-33.3mm}%
\begin{array}{c}
E_6 \\
\downarrow
\end{array}
\end{gather*}
\vspace*{-8mm}
\begin{gather*}
\bm{\Omega} =
\left(
\begin{array}{
cccccc%
>{\columncolor{red!20}}c%
cccccccc
}
1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0
& 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0
& 0 & 0 & 0 & 0 & 0 \\
1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 0
& 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 1
& 0 & 0 & 0 & 0 & 0 \\
1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0
& 1 & 1 & 0 & 1 & 0 \\
0 & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 0
& 0 & 1 & 1 & 0 & 1
\end{array}
\right),
\hspace*{7mm}
\bm{s} \in \text{span} \mleft\{
\bm{\Omega} \mright\}
\end{gather*}
\vspace*{5mm}
\newcommand{\preperr}[1]{
\gate[style={fill=blue!20}]{\scriptstyle #1}
}
\newcommand{\measerr}{\gate[style={fill=blue!20}]{\phantom{1}}}
\newcommand{\noise}{
\gate[style={noisy}]{\text{\small X}}%
\setwiretype{n}%
\wire[l][1]{q}
}
\newcommand{\redwire}[1]{
\wire[r][#1][style={draw=red, line width=1.5pt}]{q}
}
\newcommand{\redtarg}{
\targ[style={draw=red}]{}%
\setwiretype{n}%
\wire[l][1]{q}
}
\newcommand{\redctrl}[1]{
\ctrl[style={draw=red,fill=red,line width=1.5pt}]{#1}
}
\newcommand{\redmeter}{\meter[style={draw=red,fill=red!20}]{}}
\tikzset{
noisy/.style={
starburst,
starburst point height=2.5mm,
fill=red!25, draw=red!85!black,
line width=1.5pt,
inner xsep=-2pt, inner ysep=-2pt
},
}
\centering
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=3mm, wire types={q,q,q,q,q,n,n,n,n}]
\lstick[3]{$\ket{\psi}_\text{L}$} & \preperr{E_0} & \ctrl{3} & & & & \preperr{E_5} & & \ctrl{5} & & & & \preperr{E_{10}} & & \ctrl{7} & & & & & & \\
& \preperr{E_1} & & \ctrl{2} & \ctrl{3} & & \noise\redwire{14} & & & \redctrl{4} & \redctrl{5} & & \preperr{E_{11}} & & & \redctrl{6} & \redctrl{7} & & & & \\
& \preperr{E_2} & & & & \ctrl{2} & \preperr{E_7} & & & & & \ctrl{4} & \preperr{E_{12}} & & & & & \ctrl{6} & & & \\
\lstick{$\ket{0}_{\text{A}_1}$} & & \targ{} & \targ{} & & & & & & & & & & & & & & & \preperr{E_3} & \meter{} & \setwiretype{c} \\
\lstick{$\ket{0}_{\text{A}_2}$} & & & & \targ{} & \targ{} & & & & & & & & & & & & & \preperr{E_4} & \meter{} & \setwiretype{c} \\
& & & & & & \lstick{$\ket{0}_{\text{A}_3}$} & \setwiretype{q} & \targ{} & \redtarg\redwire{10} & & & & & & & & & \preperr{E_8} & \redmeter\wire[r][1][style={draw=red,double, line width=1.5pt}]{q} & \setwiretype{n} \\
& & & & & & \lstick{$\ket{0}_{\text{A}_4}$} & \setwiretype{q} & & & \redtarg\redwire{9} & \targ{} & & & & & & & \preperr{E_9} & \redmeter\wire[r][1][style={draw=red,double, line width=1.5pt}]{q} & \setwiretype{n} \\
& & & & & & & & & & & & \lstick{$\ket{0}_{\text{A}_5}$} & \setwiretype{q} & \targ{} & \redtarg\redwire{4} & & & \preperr{E_{13}} & \redmeter\wire[r][1][style={draw=red,double, line width=1.5pt}]{q} & \setwiretype{n} \\
& & & & & & & & & & & & \lstick{$\ket{0}_{\text{A}_6}$} & \setwiretype{q} & & & \redtarg\redwire{3} & \targ{} & \preperr{E_{14}} & \redmeter\wire[r][1][style={draw=red,double, line width=1.5pt}]{q} & \setwiretype{n}
\end{quantikz}
% tex-fmt: on
\caption{
Repeated syndrome extraction circuit for the three-qubit
repetition code under phenomenological noise.
}
\end{figure}
%%%%%%%%%%%%%%%%
\subsection{Detector Error Matrix}
\label{subsec:Detector Error Matrix}
% Core idea
% TODO: Make this a proper definition?
Instead of using the measurements as parity indicators directly, we
may wish to combine them in some way.
We call such combinations \emph{detectors}.
Formally, a detector is a parity constraint on a set of measurement
outcomes \cite[Def.~2.1]{derks_designing_2025}.
\content{Detector matrix}
\content{Detector error matrix}
\content{One way of defining the detectors is ...}
\begin{figure}[H]
\centering
\tikzset{
gate/.style={
draw, %line width=1pt,
minimum height=2cm,
}
}
% tex-fmt: off
\begin{quantikz}[row sep=2mm, column sep=4mm, wire types={q,q,q,n,n,n}]
\lstick[3]{$\ket{\psi}_\text{L}$} & \gate[3]{\text{SE}_1} & & \gate[3]{\text{SE}_2} & & \gate[3]{\text{SE}_3} & & \gate[3]{\text{SE}_4} & \\
& & & & & & & & & \setwiretype{n} & \ldots \\
& \wire[d][3]{c} & & \wire[d][1]{c} & & \wire[d][1]{c} & & \wire[d][1]{c} & \\
& \ctrl[wire=c]{0}\wire[r][1]{c} & \wire[d][1]{c} & \ctrl[vertical wire=c]{1}\wire[r][1]{c} & \wire[d][1]{c} & \ctrl[vertical wire=c]{1}\wire[r][1]{c} & \wire[d][1]{c} & \ctrl[vertical wire=c]{1}\wire[r][1]{c} & \\
& & \wire[r][1]{c} & \targ{}\wire[d][1]{c} & \wire[r][1]{c} & \targ{}\wire[d][1]{c} & \wire[r][1]{c} & \targ{}\wire[d][1]{c} & \\
& \gate[1]{\bm{D}_1} & & \gate[1]{\bm{D}_2} & & \gate[1]{\bm{D}_3} & & \gate[1]{\bm{D}_4} & \\
\end{quantikz}
% tex-fmt: on
\caption{Construction of detectors from measurements in the general case.}
\end{figure}
\content{The three-qubit repetition code as an exmaple}
\begin{figure}[H]
\centering
\hspace*{-5mm}
\begin{minipage}{0.42\textwidth}
\newcommand{\redwire}[1]{
\wire[r][#1][style={draw=red, line width=1.5pt, double}]{q}
}
\newcommand{\inwire}{
\wire[l][1][style={draw=red, line width=1.5pt}]{q}
}
\newcommand{\redtarg}{
\targ[style={draw=red,line width=1.5pt}]{}%
\setwiretype{n}%
}
\newcommand{\redctrl}[1]{
\ctrl[style={draw=red,fill=red, line width=1.5pt}]{0}%
\wire[d][#1][style={draw=red, line width=1.5pt, double}]{q}
}
\newcommand{\redmeter}{\meter[style={draw=red,fill=red!20}]{}}
\newcommand{\redgate}[1]{\gate[style={draw=red,fill=red!20}]{\textcolor{red}{#1}}}
% tex-fmt: off
\begin{quantikz}[row sep=4mm, column sep=3mm, wire types={n,n,n,n,n,n}]
& \meter{}\wire[l][1]{q}\wire[r][1]{c} & \setwiretype{c} & & & \ctrl[vertical wire=c]{2} & & \gate{D_1} \\
& \meter{}\wire[l][1]{q}\wire[r][1]{c} & \setwiretype{c} & & & & \ctrl[vertical wire=c]{2} & \gate{D_2} \\
& \redmeter{}\inwire\redwire{6} & & \redctrl{2} & & \targ{} & & \redgate{D_3} \\
& \redmeter{}\inwire\redwire{6} & & & \redctrl{2} & & \targ{} & \redgate{D_4} \\
& \redmeter{}\inwire\redwire{2} & & \redtarg\wire[r][4]{c} & & & & \gate{D_5} \\
& \redmeter{}\inwire\redwire{3} & & & \redtarg\wire[r][3]{c} & & & \gate{D_6}
\end{quantikz}
% tex-fmt: on
\end{minipage}%
\begin{minipage}{0.56\textwidth}
\newcommand\cc{\cellcolor{orange!20}}
\begin{align*}
\bm{H} =
% tex-fmt: off
\left(\begin{array}{ccccccccccccccc}
1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
\cc{0} & \cc{0} & \cc{0} & \cc{1} & \cc{0} & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\
\cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{1} & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\
\cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{1} & \cc{0} & 1 & 1 & 0 & 1 & 0 \\
\cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{0} & \cc{1} & 0 & 1 & 1 & 0 & 1
\end{array}\right)
% tex-fmt: on
\end{align*}
\end{minipage}
\caption{Construction of detectors from the measurements of a
three-qubit repetition code.}
\label{fig:Construction of the detectors from the measurements}
\end{figure}
%%%%%%%%%%%%%%%%
\subsection{Detector Error Models}
\label{subsec:Detector Error Models}
\content{Combination of detector error matrix and noise model}
\content{Contains all information necessary for decoding
\cite[Intro.]{derks_designing_2025}}
\content{Not only useful for decoding, but also for ... (Derks et al.)}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Practical Considerations}
\label{sec:Practical Considerations}
% Intro
\content{Intro}
%%%%%%%%%%%%%%%%
\subsection{Practical Methodology}
\label{subsec:Practical Methodology}
\indent\red{[(?) Figure from presentation, showing where the LER
calculation takes place]} \\
\content{Per-round-LER explanation}
%%%%%%%%%%%%%%%%
\subsection{Stim}
\label{subsec:Stim}
\content{Circuit code heavily depends on the exact circuit construction}
\content{Not easy to predict how errors at different locations
propagate through the circuit an what detectors they affect}
\content{Stim is a software package that generates DEMs from circuits}
\content{The user still has to define the circuit themselves, and
especially the detectors \cite[Sec~2.5]{derks_designing_2025}}

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@@ -0,0 +1,2 @@
sed -i "s/Świerkowska/{\\\\'S}wierkowska/" bibliography.bib
sed -Ezi "s/\s(abstract|note|urldate|url|keywords|file) = \{[^}]*(\{[^}]*\}[^}]*)*\},?\n//g" bibliography.bib

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

View File

@@ -6,27 +6,37 @@
\usepackage{amsfonts}
\usepackage{mleftright}
\usepackage{bm}
\usepackage{bbm}
\usepackage{tikz}
\usepackage{xcolor}
\usepackage{pgfplots}
\pgfplotsset{compat=newest}
\usepackage{acro}
\usepackage{braket}
\usepackage{listings}
\usepackage{caption}
% \usepackage[
% backend=biber,
% style=ieee,
% sorting=nty,
% ]{biblatex}
\usepackage{todonotes}
% \usepackage{todonotes}
\usepackage{quantikz}
\usepackage{stmaryrd}
\usepackage{algorithm}
\usepackage[noEnd=false]{algpseudocodex}
\usepackage{nicematrix}
\usepackage{colortbl}
\usetikzlibrary{calc, positioning, arrows, fit}
\usetikzlibrary{external}
\tikzexternalize
\makeatletter
\renewcommand{\todo}[2][]{\tikzexternaldisable\@todo[#1]{#2}\tikzexternalenable}
\makeatother
% \makeatletter
% \renewcommand{\todo}[2][]{\tikzexternaldisable\@todo[#1]{#2}\tikzexternalenable}
% \makeatother
\setcounter{MaxMatrixCols}{20}
%
%
@@ -35,6 +45,8 @@
%
\newcommand{\red}[1]{\textcolor{red}{#1}}
\newcommand{\content}[1]{\noindent\indent\red{[#1]}\\}
\newcommand{\figwidth}{10cm}
\newcommand{\figheight}{7.5cm}

Binary file not shown.

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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -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
0.001,6000,0.0361666666666666,0.003065034000747535,216.99999999999957
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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@@ -0,0 +1,8 @@
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

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@@ -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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
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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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.06175,0.0052974886347837424,247.0
0.0015,2000,0.1435,0.012825457194246215,287.0
0.002,2000,0.2345,0.022022718392227913,469.0
0.0025,2000,0.4225,0.04472297597135455,845.0
0.003,2000,0.575,0.06882263455836479,1150.0
0.0035,2000,0.737,0.10532978538399707,1474.0
0.004,2000,0.857,0.14962333077738132,1714.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.06175 0.0052974886347837424 247.0
3 0.0015 2000 0.1435 0.012825457194246215 287.0
4 0.002 2000 0.2345 0.022022718392227913 469.0
5 0.0025 2000 0.4225 0.04472297597135455 845.0
6 0.003 2000 0.575 0.06882263455836479 1150.0
7 0.0035 2000 0.737 0.10532978538399707 1474.0
8 0.004 2000 0.857 0.14962333077738132 1714.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0478333333333333,0.004076268495095681,286.9999999999998
0.0015,2000,0.1135,0.009989285108149337,227.0
0.002,2000,0.1975,0.018168216629754652,395.0
0.0025,2000,0.3355,0.033486520196851055,671.0
0.003,2000,0.4805,0.0531116004427149,961.0
0.0035,2000,0.6645,0.08699243286672087,1329.0
0.004,2000,0.8145,0.13098222531638515,1629.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0478333333333333 0.004076268495095681 286.9999999999998
3 0.0015 2000 0.1135 0.009989285108149337 227.0
4 0.002 2000 0.1975 0.018168216629754652 395.0
5 0.0025 2000 0.3355 0.033486520196851055 671.0
6 0.003 2000 0.4805 0.0531116004427149 961.0
7 0.0035 2000 0.6645 0.08699243286672087 1329.0
8 0.004 2000 0.8145 0.13098222531638515 1629.0

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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
0.001,2000,0.138,0.012298745249961884,276.0
0.0015,2000,0.2515,0.023851292168152738,503.0
0.002,2000,0.4005,0.04174204711940421,801.0
0.0025,2000,0.5515,0.06463696262955332,1103.0
0.003,2000,0.7215,0.10105021415209858,1443.0
0.0035,2000,0.854,0.14815076449150033,1708.0
0.004,2000,0.9375,0.2062994740159002,1875.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 2000 0.138 0.012298745249961884 276.0
3 0.0015 2000 0.2515 0.023851292168152738 503.0
4 0.002 2000 0.4005 0.04174204711940421 801.0
5 0.0025 2000 0.5515 0.06463696262955332 1103.0
6 0.003 2000 0.7215 0.10105021415209858 1443.0
7 0.0035 2000 0.854 0.14815076449150033 1708.0
8 0.004 2000 0.9375 0.2062994740159002 1875.0

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physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.061,0.005231252406122322,244.0
0.0015,2000,0.129,0.011443461592906767,258.0
0.002,2000,0.2375,0.022342685193895928,475.0
0.0025,2000,0.39,0.040354525526934304,780.0
0.003,2000,0.5625,0.06657053735419127,1125.0
0.0035,2000,0.72,0.10064772839754166,1440.0
0.004,2000,0.8455,0.14412427850801302,1691.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.061 0.005231252406122322 244.0
3 0.0015 2000 0.129 0.011443461592906767 258.0
4 0.002 2000 0.2375 0.022342685193895928 475.0
5 0.0025 2000 0.39 0.040354525526934304 780.0
6 0.003 2000 0.5625 0.06657053735419127 1125.0
7 0.0035 2000 0.72 0.10064772839754166 1440.0
8 0.004 2000 0.8455 0.14412427850801302 1691.0

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