Wwrite a few paragraphs on the window generation/decoding
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@@ -376,33 +376,62 @@ explicitly work with the \ac{dem} formalism.
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\subsection{Window Generation}
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\subsection{Algorithm}
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\label{subsec:Window Generation}
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\label{subsec:Algorithm}
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In this section, we will examine the methodology by which a detector
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In this section, we will examine the methodology by which a detector
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error matrix is divided into overlapping windows.
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error matrix is divided into overlapping windows.
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The algorithm detailed here follows \cite{kang_quits_2025}, whose
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The algorithm detailed here follows \cite{kang_quits_2025}, whose
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work is in turn based on \cite{huang_increasing_2024}.
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work is in turn based on \cite{huang_increasing_2024}.
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\content{Possibly go into the fact that current sliding-window
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% Very high-level overview
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approaches don't differentiate clearly between the sliding-window
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part and the decoder part. This work aims to extend the
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sliding-window part in a general fashion that is compatible with many
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different decoder parts. Combine this with QUITS modular structure
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for sliding window decoding}
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% High-level overview of Sliding-Window decoding
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Sliding-window decoding is made possible by the time-like structure
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of the syndrome extraction circuitry.
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This is epecially clearly visible under the \ac{dem} formalism, where
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this manifests as a block-diagonal structure of the detector
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error matrix $\bm{H}$.
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Note that this presupposes a choice of detectors as seen in
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\Cref{subsec:Detector Error Matrix}.
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This block-diagonal structure introduces some locality in the
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interdependence between \acp{vn} and \acp{cn}.
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For each local set of \acp{vn}, there is only a local set of connected \acp{cn}.
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We exploit this fact by cutting the matrix into overlapping windows.
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\Cref{fig:windowing_pcm} depicts this process.
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\content{Benefits of sliding-window decoding (lower latency due to
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% High-level overview
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earlier decoding start)}
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\content{Why it works (block diagonal structure $\rightarrow$ ``Done
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with processing'' some VNs)}
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% Detailed explanation of sliding-window decoding
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How the locality is leveraged can be understood by considering the
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decoding process.
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After decoding a window, there is a subset of \acp{cn} that no longer
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contribute to the decoding process, as they do not share any \acp{vn}
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with the \acp{cn} of subsequent windows.\\
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\content{Commit VNs}
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\content{Benefit of this approach (as stated above: earlier decoding start)}
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\content{We look at rows not columns}
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% W and F
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\content{Define W}
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\content{Define F}
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There are two degrees of freedom in how we perform the windowing.
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The \emph{window size} $W \in \mathbb{N}$ represents the number of
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syndrome extraction rounds lumped into one window.
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The \emph{step size} $F \in \mathbb{N}$ represents the number of
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syndrome extraction rounds passed over before starting the next window.
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$W$ controls the size of the windows while $F$ controls the overlap
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between windows.
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% Why we look at rows, not columns
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As illustrated in \Cref{fig:windowing_pcm}, $W$ and $F$ control the
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window dimensions and locactions by defining the related \acp{cn},
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not the \acp{vn}.
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This is because while the number of overall \acp{cn} is only affected
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by the choice of the underlying code and the number of syndrome
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measurement rounds, the number of \acp{vn} depends on the noise model
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and is difficult to predict beforehand.
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% How we get the corresponding rows and columns
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\content{How we get the rows}
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\content{Explain how we get the columns once we know the rows}
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\content{Explain how we get the columns once we know the rows}
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\content{\textbf{General note}: Mathematical definitions where possible}
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\content{\textbf{General note}: Mathematical definitions where possible}
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@@ -415,6 +444,7 @@ with processing'' some VNs)}
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% Complete process
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% Complete process
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\content{(?) Proper algorithm definition?}
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\content{1. Decode window}
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\content{1. Decode window}
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\content{2. Commit VN estimates}
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\content{2. Commit VN estimates}
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\content{3. Update syndrome}
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\content{3. Update syndrome}
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@@ -457,6 +487,14 @@ with processing'' some VNs)}
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\label{fig:windowing_pcm}
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\label{fig:windowing_pcm}
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\end{figure}
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\end{figure}
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% TODO: Do I need this?
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% \content{Possibly go into the fact that current sliding-window
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% approaches don't differentiate clearly between the sliding-window
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% part and the decoder part. This work aims to extend the
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% sliding-window part in a general fashion that is compatible with many
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% different decoder parts. Combine this with QUITS modular structure
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% for sliding window decoding}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Warm-Start Sliding-Window Decoding}
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\section{Warm-Start Sliding-Window Decoding}
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\label{sec:warm_start_bp}
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\label{sec:warm_start_bp}
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