|
|
|
|
@@ -72,7 +72,7 @@ problem into many smaller ones that can be solved more efficiently.
|
|
|
|
|
% warm-start decoding. Or just go into warm-start decoding)
|
|
|
|
|
We will start by briefly reviewing the existing work related to
|
|
|
|
|
sliding-window decoding,
|
|
|
|
|
before focusing on one specific incarnation.
|
|
|
|
|
before focusing on one specific realization.
|
|
|
|
|
We will then introduce a modification to the existing algorithm and
|
|
|
|
|
perform numerical simulations to evaluate it.
|
|
|
|
|
|
|
|
|
|
@@ -107,35 +107,10 @@ time dimension.
|
|
|
|
|
Each of these windows is then decoded separately.
|
|
|
|
|
|
|
|
|
|
%%%%%%%%%%%%%%%%
|
|
|
|
|
\subsection{Existing Literature}
|
|
|
|
|
\label{subsec:Existing Literature}
|
|
|
|
|
\subsection{Review of Existing Literature}
|
|
|
|
|
\label{subsec:Review of Existing Literature}
|
|
|
|
|
|
|
|
|
|
% Review of existing literature
|
|
|
|
|
|
|
|
|
|
Research on this topic has been ongoing for some time, though mostly
|
|
|
|
|
for topological codes.
|
|
|
|
|
The literature on \ac{qldpc} codes is more limited. Figure
|
|
|
|
|
\Cref{fig:literature} gives an overview of the related body of work.
|
|
|
|
|
|
|
|
|
|
\red{
|
|
|
|
|
\begin{itemize}
|
|
|
|
|
\item \cite{huang_increasing_2024} use BP+OSD,
|
|
|
|
|
\cite{gong_toward_2024} use BP+GDG
|
|
|
|
|
\item \cite{huang_improved_2023} use phenomenological noise,
|
|
|
|
|
\cite{gong_toward_2024} circuit-level noise
|
|
|
|
|
\item Go into the way the parallel decoding approaches
|
|
|
|
|
consolidate the overlap regions
|
|
|
|
|
\item \cite{huang_improved_2023} use hypegraph and lifted
|
|
|
|
|
product codes, \cite{gong_toward_2024} use BB codes
|
|
|
|
|
\item \cite{kuo_fault-tolerant_2024} use toric codes, the
|
|
|
|
|
rest of the topological papers surface codes
|
|
|
|
|
\item \cite{dennis_topological_2002} call their scheme ``overlap-add''
|
|
|
|
|
\item QUITS views sliding-window decoding more separately
|
|
|
|
|
\item Reasons for latency improvement ()
|
|
|
|
|
\end{itemize}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
\begin{figure}[H]
|
|
|
|
|
\begin{figure}[t]
|
|
|
|
|
\centering
|
|
|
|
|
|
|
|
|
|
\tikzset{
|
|
|
|
|
@@ -156,20 +131,22 @@ The literature on \ac{qldpc} codes is more limited. Figure
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
\tikzexternaldisable
|
|
|
|
|
\begin{tikzpicture}[node distance = 0mm and 0mm]
|
|
|
|
|
% tex-fmt: off
|
|
|
|
|
\node[heading, minimum width=15mm, fill=gray!25] (code) {Code};
|
|
|
|
|
\node[heading, below right=1mm and -5mm of code, fill=orange!20] (top) {Topological};
|
|
|
|
|
\node[heading, below right=42mm and -5mm of code, fill=orange!20] (qldpc) {QLDPC};
|
|
|
|
|
\node[heading, minimum width=15mm, fill=gray!25] (code) {Code};
|
|
|
|
|
\node[heading, below right=2mm and -5mm of code, fill=orange!20] (top) {Topological};
|
|
|
|
|
\node[heading, below right=45mm and -5mm of code, fill=orange!20] (qldpc) {QLDPC};
|
|
|
|
|
|
|
|
|
|
\node[literature, below right=0mm and -12mm of top] (dennis) {\cite{dennis_topological_2002}};
|
|
|
|
|
\node[literature, below right=1mm and -12mm of top] (dennis) {\cite{dennis_topological_2002}};
|
|
|
|
|
\node[literature, below=of dennis] (tan) {\cite{tan_scalable_2023}};
|
|
|
|
|
\node[literature, below=of tan] (skoric) {\cite{skoric_parallel_2023}};
|
|
|
|
|
\node[literature, below=of skoric] (bombin) {\cite{bombin_modular_2023}};
|
|
|
|
|
\node[literature, below=of bombin] (kuo) {\cite{kuo_fault-tolerant_2024}};
|
|
|
|
|
|
|
|
|
|
\node[literature, below right=0mm and -12mm of qldpc] (huang) {\cite{huang_improved_2023},\cite{huang_increasing_2024}};
|
|
|
|
|
\node[literature, below right=1mm and -12mm of qldpc] (huang) {\cite{huang_improved_2023},\cite{huang_increasing_2024}};
|
|
|
|
|
\node[literature, below=of huang] (gong) {\cite{gong_toward_2024}};
|
|
|
|
|
\node[literature, below=of gong] (kang) {\cite{kang_quits_2025}};
|
|
|
|
|
|
|
|
|
|
\coordinate (code-anchor) at ($(code.south) + (-2mm,0)$);
|
|
|
|
|
\coordinate (top-anchor) at ($(top.south) + (-5mm,0)$);
|
|
|
|
|
@@ -186,6 +163,7 @@ The literature on \ac{qldpc} codes is more limited. Figure
|
|
|
|
|
|
|
|
|
|
\draw (qldpc-anchor) |- (huang);
|
|
|
|
|
\draw (qldpc-anchor) |- (gong);
|
|
|
|
|
\draw (qldpc-anchor) |- (kang);
|
|
|
|
|
|
|
|
|
|
\draw [
|
|
|
|
|
line width=1pt,
|
|
|
|
|
@@ -208,15 +186,113 @@ The literature on \ac{qldpc} codes is more limited. Figure
|
|
|
|
|
decorate,
|
|
|
|
|
decoration={brace,amplitude=2mm,raise=5mm}
|
|
|
|
|
]
|
|
|
|
|
(huang.north east) -- (gong.south east)
|
|
|
|
|
(huang.north east) -- (kang.south east)
|
|
|
|
|
node[midway,right,xshift=10mm]{Sequential};
|
|
|
|
|
% tex-fmt: on
|
|
|
|
|
\end{tikzpicture}
|
|
|
|
|
\tikzexternalenable
|
|
|
|
|
|
|
|
|
|
\caption{Overview of literature on sliding-window decoding.}
|
|
|
|
|
\label{fig:literature}
|
|
|
|
|
\end{figure}
|
|
|
|
|
|
|
|
|
|
% Some general notes
|
|
|
|
|
|
|
|
|
|
\Cref{fig:literature} gives an overview over the existing body of work
|
|
|
|
|
related to sliding-window decoding.
|
|
|
|
|
The papers \cite{huang_improved_2023} and \cite{huang_increasing_2024} are
|
|
|
|
|
lumped together, as they share the same content;
|
|
|
|
|
one is simply preprint published earlier.
|
|
|
|
|
We will only refer to \cite{huang_increasing_2024} in the following.
|
|
|
|
|
\cite{kang_quits_2025} is somewhat special in that the authors focus
|
|
|
|
|
more on the introduction of a new simluator framework they call
|
|
|
|
|
QUITS, rather than the performance of sliding-window decoding itself.
|
|
|
|
|
\cite{gong_toward_2024} and \cite{kang_quits_2025} have made their
|
|
|
|
|
software freely available online%
|
|
|
|
|
\footnote{
|
|
|
|
|
https://github.com/mkangquantum/quits
|
|
|
|
|
}%
|
|
|
|
|
\footnote{
|
|
|
|
|
https://github.com/gongaa/SlidingWindowDecoder
|
|
|
|
|
}.
|
|
|
|
|
A final thing to note is that \cite{dennis_topological_2002} never
|
|
|
|
|
explicitly mention sliding windows, they call their scheme
|
|
|
|
|
``overlapping recovery''.
|
|
|
|
|
|
|
|
|
|
% Topological vs QLDPC
|
|
|
|
|
|
|
|
|
|
Research has focused on two categories of \ac{qec} codes, topological
|
|
|
|
|
and \ac{qldpc} codes.
|
|
|
|
|
Most of the work on topological codes has treated surface codes,
|
|
|
|
|
with the exception of \cite{kuo_fault-tolerant_2024} where toric
|
|
|
|
|
codes were considered.
|
|
|
|
|
With regard to \ac{qldpc} codes, in \cite{huang_increasing_2024}
|
|
|
|
|
they examine \emph{hypergraph product} (\acs{hgp}) and
|
|
|
|
|
\emph{lifted-product} (\acs{lp}) codes.
|
|
|
|
|
HGP codes are constructed from the product of two classical codes,
|
|
|
|
|
while LP codes generalize this construction by additionally applying
|
|
|
|
|
a lift to reduce the qubit overhead.
|
|
|
|
|
In \cite{kang_quits_2025}, \emph{balanced product codes} (\acs{bpc})
|
|
|
|
|
are additionally considered.
|
|
|
|
|
Like HGP codes, BPC codes are derived from a product construction,
|
|
|
|
|
but exploit an additional symmetry to yield fewer physical qubits for
|
|
|
|
|
the same code parameters.
|
|
|
|
|
Finally, in \cite{gong_toward_2024} the authors explore \ac{bb} codes.
|
|
|
|
|
|
|
|
|
|
% Sequential vs parallel
|
|
|
|
|
|
|
|
|
|
After having divided the whole circuit into separate windows, the question
|
|
|
|
|
arises of how exactly to realize the decoding.
|
|
|
|
|
There are two main approaches, with differing mechanisms of reducing
|
|
|
|
|
the latency.
|
|
|
|
|
Some papers decode the sliding windows in a parallel fashion.
|
|
|
|
|
The benefit in this case is the option to more effectively utilize
|
|
|
|
|
classical hardware for decoding.
|
|
|
|
|
Others choose a sequential approach.
|
|
|
|
|
Here, decoding can start earlier, as there is no need to wait for the
|
|
|
|
|
syndrome measurements of all windows before beginning with the decoding.
|
|
|
|
|
With the exception of \cite{dennis_topological_2002}, literature
|
|
|
|
|
treating topological codes has mostly focused on parallel decoding
|
|
|
|
|
while literature treating \ac{qldpc} codes has wholely considered
|
|
|
|
|
sequential decoding.
|
|
|
|
|
|
|
|
|
|
% Deep-dive into QLDPC methods
|
|
|
|
|
|
|
|
|
|
For this work, the publications treating \ac{qldpc} codes are
|
|
|
|
|
especially interesting.
|
|
|
|
|
The experimental conditions for these are summarized in
|
|
|
|
|
\Cref{table:experimental_conditions}.
|
|
|
|
|
As we noted above, \ac{hgp} and \ac{lp} codes are considered in
|
|
|
|
|
\cite{huang_increasing_2024},
|
|
|
|
|
\ac{hgp}, \ac{lp} and \ac{bpc} codes are considered in \cite{kang_quits_2025},
|
|
|
|
|
and \ac{bb} codes are considered in \cite{gong_toward_2024}.
|
|
|
|
|
The employed noise models also differ;
|
|
|
|
|
\cite{huang_increasing_2024} use phenomenological noise, while
|
|
|
|
|
\cite{gong_toward_2024} and \cite{kang_quits_2025} use circuit-level noise.
|
|
|
|
|
Finally, \cite{gong_toward_2024} introduce their own variation of
|
|
|
|
|
\ac{bpgd}, \ac{bp} with \ac{gdg}, while \cite{huang_increasing_2024}
|
|
|
|
|
and \cite{kang_quits_2025} use \ac{bp} + \ac{osd}.
|
|
|
|
|
We would additionally like to note that only in
|
|
|
|
|
\cite{gong_toward_2024} and \cite{kang_quits_2025} do the authors
|
|
|
|
|
explicitly work with the \ac{dem} formalism.
|
|
|
|
|
|
|
|
|
|
\renewcommand{\arraystretch}{1.1}
|
|
|
|
|
\setlength{\tabcolsep}{12pt}
|
|
|
|
|
\begin{table}[t]
|
|
|
|
|
\centering
|
|
|
|
|
\caption{Experimental conditions for papers related to \ac{qldpc} codes.}
|
|
|
|
|
\vspace*{3mm}
|
|
|
|
|
\label{table:experimental_conditions}
|
|
|
|
|
\begin{tabular}{l|ccc}
|
|
|
|
|
% tex-fmt: off
|
|
|
|
|
Publication & Code & Noise Model & Decoder \\ \hline
|
|
|
|
|
\hspace{-2.5mm}\cite{huang_improved_2023},\cite{huang_increasing_2024} & \acs{hgp}, \acs{lp} & Phenomenological noise & \acs{bp} + \acs{osd} \\
|
|
|
|
|
\hspace{-2.5mm}\cite{gong_toward_2024} & \acs{bb} & Circuit-level noise & \acs{bp} + \acs{gdg} \\
|
|
|
|
|
\hspace{-2.5mm}\cite{kang_quits_2025} & \acs{hgp}, \acs{lp}, \acs{bpc} & Circuit-level noise & \acs{bp} + \ac{osd}
|
|
|
|
|
% tex-fmt: on
|
|
|
|
|
\end{tabular}
|
|
|
|
|
\end{table}
|
|
|
|
|
|
|
|
|
|
% \red{
|
|
|
|
|
% Existing work
|
|
|
|
|
% \begin{itemize}
|
|
|
|
|
@@ -299,32 +375,63 @@ The literature on \ac{qldpc} codes is more limited. Figure
|
|
|
|
|
% \end{itemize}
|
|
|
|
|
% }
|
|
|
|
|
|
|
|
|
|
\content{Possibly go into the fact that current sliding-window
|
|
|
|
|
approaches don't differentiate clearly between the sliding-window
|
|
|
|
|
part and the decoder part. This work aims to extend the
|
|
|
|
|
sliding-window part in a general fashion that is compatible with many
|
|
|
|
|
different decoder parts. Combine this with QUITS modular structure
|
|
|
|
|
for sliding window decoding}
|
|
|
|
|
|
|
|
|
|
%%%%%%%%%%%%%%%%
|
|
|
|
|
\subsection{Implementation of Sliding-Window Decoding}
|
|
|
|
|
\label{subsec:Implementation of Sliding-Window Decoding}
|
|
|
|
|
\subsection{Algorithm}
|
|
|
|
|
\label{subsec:Algorithm}
|
|
|
|
|
|
|
|
|
|
We build on the approach taken by \cite{huang_increasing_2024} and
|
|
|
|
|
\cite{gong_toward_2024}.
|
|
|
|
|
In this section, we will examine the methodology by which a detector
|
|
|
|
|
error matrix is divided into overlapping windows.
|
|
|
|
|
The algorithm detailed here follows \cite{kang_quits_2025}, whose
|
|
|
|
|
work is in turn based on \cite{huang_increasing_2024}.
|
|
|
|
|
|
|
|
|
|
% High-level overview of Sliding-Window decoding
|
|
|
|
|
% Very high-level overview
|
|
|
|
|
|
|
|
|
|
\content{Benefits of sliding-window decoding (lower latency due to
|
|
|
|
|
earlier decoding start)}
|
|
|
|
|
\content{Why it works (block diagonal structure $\rightarrow$ ``Done
|
|
|
|
|
with processing'' some VNs)}
|
|
|
|
|
Sliding-window decoding is made possible by the time-like structure
|
|
|
|
|
of the syndrome extraction circuitry.
|
|
|
|
|
This is epecially clearly visible under the \ac{dem} formalism, where
|
|
|
|
|
this manifests as a block-diagonal structure of the detector
|
|
|
|
|
error matrix $\bm{H}$.
|
|
|
|
|
Note that this presupposes a choice of detectors as seen in
|
|
|
|
|
\Cref{subsec:Detector Error Matrix}.
|
|
|
|
|
This block-diagonal structure introduces some locality in the
|
|
|
|
|
interdependence between \acp{vn} and \acp{cn}.
|
|
|
|
|
For each local set of \acp{vn}, there is only a local set of connected \acp{cn}.
|
|
|
|
|
We exploit this fact by cutting the matrix into overlapping windows.
|
|
|
|
|
\Cref{fig:windowing_pcm} depicts this process.
|
|
|
|
|
|
|
|
|
|
% Detailed explanation of sliding-window decoding
|
|
|
|
|
% High-level overview
|
|
|
|
|
|
|
|
|
|
\content{We look at rows not columns}
|
|
|
|
|
\content{Define W}
|
|
|
|
|
\content{Define F}
|
|
|
|
|
How the locality is leveraged can be understood by considering the
|
|
|
|
|
decoding process.
|
|
|
|
|
After decoding a window, there is a subset of \acp{cn} that no longer
|
|
|
|
|
contribute to the decoding process, as they do not share any \acp{vn}
|
|
|
|
|
with the \acp{cn} of subsequent windows.\\
|
|
|
|
|
\content{Commit VNs}
|
|
|
|
|
\content{Benefit of this approach (as stated above: earlier decoding start)}
|
|
|
|
|
|
|
|
|
|
% W and F
|
|
|
|
|
|
|
|
|
|
There are two degrees of freedom in how we perform the windowing.
|
|
|
|
|
The \emph{window size} $W \in \mathbb{N}$ represents the number of
|
|
|
|
|
syndrome extraction rounds lumped into one window.
|
|
|
|
|
The \emph{step size} $F \in \mathbb{N}$ represents the number of
|
|
|
|
|
syndrome extraction rounds passed over before starting the next window.
|
|
|
|
|
$W$ controls the size of the windows while $F$ controls the overlap
|
|
|
|
|
between windows.
|
|
|
|
|
|
|
|
|
|
% Why we look at rows, not columns
|
|
|
|
|
|
|
|
|
|
As illustrated in \Cref{fig:windowing_pcm}, $W$ and $F$ control the
|
|
|
|
|
window dimensions and locactions by defining the related \acp{cn},
|
|
|
|
|
not the \acp{vn}.
|
|
|
|
|
This is because while the number of overall \acp{cn} is only affected
|
|
|
|
|
by the choice of the underlying code and the number of syndrome
|
|
|
|
|
measurement rounds, the number of \acp{vn} depends on the noise model
|
|
|
|
|
and is difficult to predict beforehand.
|
|
|
|
|
|
|
|
|
|
% How we get the corresponding rows and columns
|
|
|
|
|
|
|
|
|
|
\content{How we get the rows}
|
|
|
|
|
\content{Explain how we get the columns once we know the rows}
|
|
|
|
|
\content{\textbf{General note}: Mathematical definitions where possible}
|
|
|
|
|
|
|
|
|
|
@@ -337,13 +444,14 @@ with processing'' some VNs)}
|
|
|
|
|
|
|
|
|
|
% Complete process
|
|
|
|
|
|
|
|
|
|
\content{(?) Proper algorithm definition?}
|
|
|
|
|
\content{1. Decode window}
|
|
|
|
|
\content{2. Commit VN estimates}
|
|
|
|
|
\content{3. Update syndrome}
|
|
|
|
|
\content{4. Decode next window}
|
|
|
|
|
\content{(?) Explicitly mention we don't reuse existing messages?}
|
|
|
|
|
|
|
|
|
|
\begin{figure}[H]
|
|
|
|
|
\begin{figure}[t]
|
|
|
|
|
\centering
|
|
|
|
|
|
|
|
|
|
\hspace*{-114mm}%
|
|
|
|
|
@@ -361,7 +469,7 @@ with processing'' some VNs)}
|
|
|
|
|
\begin{tikzpicture}
|
|
|
|
|
\draw[{Latex}-{Latex}, line width=.7pt] (0, -0.75mm) -- (0, 5mm);
|
|
|
|
|
\draw[line width=1pt] (-1mm,-0.75mm) --
|
|
|
|
|
(3mm,-0.75mm);
|
|
|
|
|
(3mm,-0.75mm);
|
|
|
|
|
\draw[line width=1pt] (-1mm,5mm) -- (3mm,5mm);
|
|
|
|
|
\node[left] at (-2mm,2.125mm) {$\sim W$};
|
|
|
|
|
|
|
|
|
|
@@ -379,6 +487,14 @@ with processing'' some VNs)}
|
|
|
|
|
\label{fig:windowing_pcm}
|
|
|
|
|
\end{figure}
|
|
|
|
|
|
|
|
|
|
% TODO: Do I need this?
|
|
|
|
|
% \content{Possibly go into the fact that current sliding-window
|
|
|
|
|
% approaches don't differentiate clearly between the sliding-window
|
|
|
|
|
% part and the decoder part. This work aims to extend the
|
|
|
|
|
% sliding-window part in a general fashion that is compatible with many
|
|
|
|
|
% different decoder parts. Combine this with QUITS modular structure
|
|
|
|
|
% for sliding window decoding}
|
|
|
|
|
|
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
|
|
|
\section{Warm-Start Sliding-Window Decoding}
|
|
|
|
|
\label{sec:warm_start_bp}
|
|
|
|
|
@@ -807,7 +923,7 @@ standard circuit-based depolarizing noise model, etc.)}
|
|
|
|
|
{3/KITred/triangle*,4/KITblue/diamond*,5/KITorange/square*} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, solid, mark
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=physical_p,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -888,7 +1004,7 @@ options={fill=\col}, \col]
|
|
|
|
|
{3/KITred/triangle*,4/KITblue/diamond*,5/KITorange/square*} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, solid, mark
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=physical_p,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -971,7 +1087,7 @@ options={fill=\col}, \col]
|
|
|
|
|
{3/KITred/triangle*,2/KITblue/diamond*,1/KITorange/square*} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, solid, mark
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
options={fill=\col}, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=physical_p,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1039,7 +1155,7 @@ options={fill=\col}, \col]
|
|
|
|
|
{3/KITred/triangle,4/KITblue/diamond,5/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1110,7 +1226,7 @@ forget plot, \col]
|
|
|
|
|
{3/KITred/triangle,2/KITblue/diamond,1/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1148,7 +1264,7 @@ forget plot, \col]
|
|
|
|
|
under circuit-level noise.
|
|
|
|
|
$12$ rounds of syndrome extraction were performed and
|
|
|
|
|
standard circuit-based depolarizing noise was chosen as the
|
|
|
|
|
noise model.
|
|
|
|
|
noise model.
|
|
|
|
|
The physical error probabilty was fixed to $0.0025$.
|
|
|
|
|
}
|
|
|
|
|
\end{figure}
|
|
|
|
|
@@ -1307,7 +1423,7 @@ noise model.
|
|
|
|
|
information.
|
|
|
|
|
$12$ rounds of syndrome extraction were performed and
|
|
|
|
|
standard circuit-based depolarizing noise was chosen as the
|
|
|
|
|
noise model.
|
|
|
|
|
noise model.
|
|
|
|
|
}
|
|
|
|
|
\end{figure}
|
|
|
|
|
|
|
|
|
|
@@ -1353,7 +1469,7 @@ noise model.
|
|
|
|
|
{3/KITred/triangle,4/KITblue/diamond,5/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1424,7 +1540,7 @@ forget plot, \col]
|
|
|
|
|
{3/KITred/triangle,2/KITblue/diamond,1/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1469,7 +1585,7 @@ forget plot, \col]
|
|
|
|
|
included only the messages on the Tanner graph.
|
|
|
|
|
$12$ rounds of syndrome extraction were performed and
|
|
|
|
|
standard circuit-based depolarizing noise was chosen as the
|
|
|
|
|
noise model.
|
|
|
|
|
noise model.
|
|
|
|
|
The physical error probabilty was fixed to $0.0025$.
|
|
|
|
|
}
|
|
|
|
|
\end{figure}
|
|
|
|
|
@@ -1624,7 +1740,7 @@ noise model.
|
|
|
|
|
included only the messages on the Tanner graph.
|
|
|
|
|
$12$ rounds of syndrome extraction were performed and
|
|
|
|
|
standard circuit-based depolarizing noise was chosen as the
|
|
|
|
|
noise model.
|
|
|
|
|
noise model.
|
|
|
|
|
}
|
|
|
|
|
\end{figure}
|
|
|
|
|
|
|
|
|
|
@@ -1670,7 +1786,7 @@ noise model.
|
|
|
|
|
{3/KITred/triangle,4/KITblue/diamond,5/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1741,7 +1857,7 @@ forget plot, \col]
|
|
|
|
|
{3/KITred/triangle,2/KITblue/diamond,1/KITorange/square} {
|
|
|
|
|
\edef\temp{\noexpand
|
|
|
|
|
\addplot+[mark=\mark, densely dashed,
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
forget plot, \col]
|
|
|
|
|
table[
|
|
|
|
|
col sep=comma, x=max_iter,
|
|
|
|
|
y=LER_per_round,
|
|
|
|
|
@@ -1786,9 +1902,8 @@ forget plot, \col]
|
|
|
|
|
included only the messages on the Tanner graph.
|
|
|
|
|
$12$ rounds of syndrome extraction were performed and
|
|
|
|
|
standard circuit-based depolarizing noise was chosen as the
|
|
|
|
|
noise model.
|
|
|
|
|
noise model.
|
|
|
|
|
The physical error probabilty was fixed to $0.0025$.
|
|
|
|
|
}
|
|
|
|
|
\end{figure}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|