Wwrite a few paragraphs on the window generation/decoding

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2026-05-01 11:47:21 +02:00
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@@ -376,33 +376,62 @@ explicitly work with the \ac{dem} formalism.
% }
%%%%%%%%%%%%%%%%
\subsection{Window Generation}
\label{subsec:Window Generation}
\subsection{Algorithm}
\label{subsec:Algorithm}
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}.
\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}
% Very high-level overview
% High-level overview of Sliding-Window decoding
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.
\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)}
% High-level overview
% Detailed explanation of sliding-window decoding
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)}
\content{We look at rows not columns}
\content{Define W}
\content{Define F}
% 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}
@@ -415,6 +444,7 @@ with processing'' some VNs)}
% Complete process
\content{(?) Proper algorithm definition?}
\content{1. Decode window}
\content{2. Commit VN estimates}
\content{3. Update syndrome}
@@ -457,6 +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}