Finish intro

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% Intro to quantum computing
% TODO: Rephrase
In 1982, Richard Feynman, motivated by the difficulty of simulating
quantum-mechanical systems on classical hardware, put forward the
idea of building computers from quantum hardware themselves
\cite{feynman_simulating_1982}.
The use of such quantum computers has since been shown to offer promising
prospects not only with regard to simulating quantum systems but also
for solving certain kinds of problems that are classicaly intractable.
for solving certain kinds of problems that are classically intractable.
The most prominent example is Shor's algorithm for integer
factorization \cite{shor_algorithms_1994}.
@@ -73,12 +72,12 @@ subsequent decoding process on the measured syndrome.
Another difference between \ac{qec} and classical channel coding is
the resource constraints.
For QEC, low latency matters more than low overall computational
For \ac{qec}, low latency matters more than low overall computational
complexity, due to the backlog problem
\cite[Sec.~II.G.3.]{terhal_quantum_2015}: Some gates may turn
\cite[Sec.~II.G.3.]{terhal_quantum_2015}: Certain gates turn
single-qubit errors into multi-qubit ones, so errors must be
corrected beforehand.
A QEC system that is too slow accumulates a backlog at these points,
A \ac{qec} system that is too slow accumulates a backlog at these points,
causing exponential slowdown.
Several code constructions have been proposed for \ac{qec} codes over the years.
@@ -87,43 +86,75 @@ standard for experimental applications for a long time
\cite[Sec.~I]{koutsioumpas_colour_2025}, due to their
reliance on only local connections between qubits
\cite[Sec.~5]{roffe_decoding_2020}.
Recently, \ac{qldpc} codes have been getting increasingly more
Recently, \ac{qldpc} codes have been getting increasing
attention as they have been shown to offer comparable thresholds with
substantially improved encoding rates \cite[Sec.~1]{bravyi_high-threshold_2024}.
\ac{qldpc} codes are generally decoded using a syndrome-based variant
of the \ac{bp} algorithm \cite[Sec.~1]{roffe_decoding_2020}.
We focus on \ac{qldpc} codes in our work and specifically \ac{bb} codes,
as they are promising candidates for practical QEC due to their high
encoding rates, large minimum distances, and short-depth syndrome
extraction circuits \cite[Sec.~1]{bravyi_high-threshold_2024}.
% DEMs and fault tolerance
\content{Syndrome extraction can also be faulty -> Need for fault tolerance}
\content{Have to repeat syndrome measurements}
\content{DEMs one way of implementing fault tolerance: Model more
error locations -> Larger resulting codes}
\content{Literature deals with latency problem for fault tolerance by
sliding-window decoding}
The syndrome extraction itself is implemented on quantum hardware and
is therefore subject to the same noise as the data qubits.
As a consequence, the \ac{qec} procedure, meant to protect the quantum
state, itself introduces new \emph{internal errors}.
A procedure is called \emph{fault-tolerant} if it remains effective
even in the presence of these internal errors
\cite[Sec.~4]{gottesman_introduction_2009}.
To deal with internal errors that flip syndrome bits, multiple rounds
of syndrome measurements are performed.
One approach of implementing fault tolerance is using \acp{dem}.
A \ac{dem} abstracts away the underlying circuit,
focusing only on the relationship between possible errors
and their effects on the syndrome \cite[Sec.~1.4.3]{higgott_practical_2024}.
A \emph{detector error matrix} is generated from the circuit, which is
used for decoding instead of the original check matrix.
Decoding under a \ac{dem} poses a challenge with respect to the
latency constraint.
This is because the detector error matrix is much larger than the
check matrix of the underlying code, since it needs to represent many
more error locations.
For example, in our experiments using the $\llbracket 144,12,12
\rrbracket$ \ac{bb} code with $12$ syndrome measurement rounds, the
number of \acp{vn} grew from $144$ to $9504$ and the number of
\acp{cn} grew from $72$ to $1008$.
To keep the latency of \ac{dem} decoding manageable, one approach is
\emph{sliding-window decoding}.
Instead of decoding on the entire detector error matrix at once,
it is partitioned into several overlapping windows.
Once decoding of one window is complete, error estimates on the initial part
that is no longer needed are committed, and the next window is processed.
This way, decoding can start as soon as the syndrome bits required
for the first window have been extracted.
The idea originates with the \emph{overlapping recovery} scheme
proposed for the surface code in
\cite[Sec.~IV.B]{dennis_topological_2002} and has since been studied
for surface and toric codes \cite{kuo_fault-tolerant_2024} as well as
for \ac{qldpc} codes under both phenomenological and circuit-level
noise \cite{huang_increasing_2024,gong_toward_2024,kang_quits_2025}.
% Reseach gap + our work
\content{Use BP for decoding, but has convergence issues -> Modify BP}
\content{We note a striking similarity between sliding-window
decoding for DEMs and the way SC-LDPC codes are decoded}
\content{Extend QEC sliding-window decoding by warm start, inspired
by SC-LDPC decoders}
The existing realizations of sliding-window decoding for \ac{qec}
We observe a structural similarity between sliding-window decoding for
\acp{dem} and window decoding for \ac{sc}-\acs{ldpc} codes.
In contrast to the latter, however, where \ac{bp} messages are
carried between windows \cite[Sec.~III.~C.]{hassan_fully_2016},
the existing realizations of sliding-window decoding for \ac{qec}
discard the soft information produced inside one window before moving
on to the next, in contrast to the analogous \ac{sc}-\ac{ldpc}
decoders, which carry messages between windows
\cite[Sec.~III.~C.]{hassan_fully_2016}.
This thesis investigates whether the same idea can be carried over to
the \ac{qec} setting.
to the next.
We propose \emph{warm-start sliding-window decoding}, in which the
\ac{bp} messages from the overlap region of the previous window are
reused to initialize \ac{bp} in the current window in place of the
standard cold-start initialization.
We formulate the warm start first for plain \ac{bp} and then for
\ac{bpgd}, where some care is needed in deciding which information to
carry over.
\ac{bpgd}, a variant of \ac{bp} with better convergence properties
for \ac{qec} codes.
The decoders are evaluated by Monte Carlo simulation on the
$\llbracket 144,12,12 \rrbracket$ \ac{bb} code under standard
circuit-based depolarizing noise over $12$ syndrome extraction rounds.
@@ -131,140 +162,6 @@ The main finding is that warm-starting yields a consistent
improvement at low iteration budgets, which is the regime relevant for
low-latency operation.
% The need for fault tolerance
% A naive picture of \ac{qec} treats the syndrome extraction circuit as
% ideal and only considers errors on the data qubits.
% In reality, every gate, every ancilla, and every measurement involved
% in extracting the syndrome can itself fail, introducing new faults
% into the procedure that is supposed to correct them
% \cite[Sec.~III]{shor_scheme_1995}.
% A \ac{qec} procedure is called \emph{fault-tolerant} if it remains
% effective in the presence of these internal faults
% \cite[Sec.~4]{gottesman_introduction_2009}.
% Fault tolerance
% The standard formal definition requires the number of output errors
% to remain bounded as long as the combined number of input and
% internal errors does not exceed the correction capability of the code
% \cite[Def.~4.2]{derks_designing_2025}.
% To deal with internal errors that flip syndrome bits, multiple rounds
% of syndrome measurements are performed, and the resulting space-time
% history of detector outcomes is decoded jointly.
% The probabilities of errors at each location in the circuit are
% collected in a \emph{noise model}.
% The most general such model, in which an arbitrary Pauli error is
% allowed after each gate, is referred to as \emph{circuit-level noise}
% \cite[Def.~2.5]{derks_designing_2025} and is the noise model that
% should be used for fault-tolerance simulations
% \cite[Sec.~4.2]{derks_designing_2025}.
% DEMs
% The combination of circuit-level noise and multiple syndrome
% measurement rounds yields a complicated, code- and circuit-specific
% decoding problem.
% A recent line of work argues that this problem is most cleanly
% expressed through a \acf{dem} \cite[Sec.~6]{derks_designing_2025}.
% A \ac{dem} abstracts away the underlying circuit and lists the
% independent error mechanisms together with the detectors they flip
% and the logical observables they affect.
% From the decoder's perspective, decoding under a \ac{dem} is again a
% classical decoding problem on a parity-check matrix, with the
% detectors playing the role of \acfp{cn} and the error mechanisms
% playing the role of \acfp{vn}.
% The standard tool for generating \acp{dem} from arbitrary stabilizer
% circuits is Stim \cite{gidney_stim_2021}, in which the \ac{dem}
% formalism was originally introduced.
% The issues with deocoding under DEMs
% For \ac{qec}, the binding constraint on the decoder is latency, not
% raw computational complexity.
% This is the \emph{backlog problem}: certain gates can transform
% existing single-qubit errors into multi-qubit errors, and any
% correction must be applied before such gates are reached.
% A decoder that fails to keep up with the rate at which the hardware
% produces syndromes leads to an exponential slowdown of the computation
% \cite[Sec.~II.G.3.]{terhal_quantum_2015}.
% Decoding under a \ac{dem} aggravates this constraint, because the
% matrix that results from unrolling several rounds of syndrome
% extraction is much larger than the parity-check matrix of the
% underlying code.
% Each error mechanism in the circuit becomes a separate \ac{vn} and
% each detector becomes a separate \ac{cn}.
% For the $\llbracket 144,12,12 \rrbracket$ \acf{bb} code
% \cite[Sec.~3]{bravyi_high-threshold_2024} with $12$ syndrome
% measurement rounds, the number of \acp{vn} grows from $144$ to $9504$
% and the number of \acp{cn} grows from $72$ to $1008$.
% Exiting solutions to these issues (sliding-window decoding + BP modifications)
% The dominant strategy for keeping the latency of \ac{dem} decoding
% manageable is \emph{sliding-window decoding}.
% Instead of decoding the entire space-time history at once, the
% decoder operates on a window that spans only a few syndrome
% measurement rounds.
% After each round, the window slides forward, and the corrections in
% the part of the previous window that is no longer needed are committed.
% The idea originates with the \emph{overlapping recovery} scheme
% proposed for the surface code in \cite[Sec.~IV.B]{dennis_topological_2002}
% and has since been studied for surface and toric codes
% \cite{kuo_fault-tolerant_2024} as well as for \ac{qldpc} codes under
% both phenomenological and circuit-level noise
% \cite{huang_increasing_2024,gong_toward_2024,kang_quits_2025}.
% The structure of the decoding problem inside each window is
% reminiscent of \acf{sc}-\acf{ldpc} decoding from classical
% communications \cite[Intro.]{costello_spatially_2014}, where similar
% windowing techniques are used and where soft information is passed
% between consecutive windows
% \cite[Sec.~III.~C.]{hassan_fully_2016}.
% We focus on QLDPC codes
% In this work we focus on \acf{qldpc} codes, of which the \ac{bb} code
% mentioned above is one example.
% \ac{qldpc} codes have emerged as leading candidates for practical
% \ac{qec} due to their high encoding rates and large minimum distances
% at short syndrome-extraction-circuit depths
% \cite[Sec.~1]{bravyi_high-threshold_2024}.
% The natural decoder for them is \acf{bp}, which is well suited to
% sparse parity-check matrices and admits an efficient and parallel
% implementation, but is known to converge poorly on quantum codes due
% to quantum degeneracy and the unavoidable short cycles in the Tanner
% graph \cite[Sec.~II.C.]{babar_fifteen_2015}\cite[Sec.~V]{roffe_decoding_2020}.
% Several modifications of \ac{bp} have been proposed to address this:
% combining \ac{bp} with \acf{osd} \cite{roffe_decoding_2020}, decoding
% multiple variations of the code in parallel as in \acf{aed}
% \cite{koutsioumpas_automorphism_2025}, or extending \ac{bp} with
% guided decimation as in \acf{bpgd} \cite{yao_belief_2024}.
% Contributions of this Thesis
% The existing realizations of sliding-window decoding for \ac{qec}
% discard the soft information produced inside one window before moving
% on to the next, in contrast to the analogous \ac{sc}-\ac{ldpc}
% decoders, which carry messages between windows
% \cite[Sec.~III.~C.]{hassan_fully_2016}.
% This thesis investigates whether the same idea can be carried over to
% the \ac{qec} setting.
%
% We propose \emph{warm-start sliding-window decoding}, in which the
% \ac{bp} messages from the overlap region of the previous window are
% reused to initialize \ac{bp} in the current window in place of the
% standard cold-start initialization.
% We formulate the warm start first for plain \ac{bp} and then for
% \ac{bpgd}, where some care is needed in deciding which information to
% carry over.
% The decoders are evaluated by Monte Carlo simulation on the
% $\llbracket 144,12,12 \rrbracket$ \ac{bb} code under standard
% circuit-based depolarizing noise over $12$ syndrome extraction rounds.
% The main finding is that warm-starting yields a consistent
% improvement at low iteration budgets, which is the regime relevant for
% fault-tolerant operation.
% Outline of the Thesis
\Cref{ch:Fundamentals} reviews the fundamentals of classical and
@@ -292,6 +189,7 @@ introduces the proposed warm-start sliding-window decoder for
plain \ac{bp} and for \ac{bpgd}, and reports numerical results on the
$\llbracket 144,12,12 \rrbracket$ \ac{bb} code.
% TODO: Possibly extend to mention specific proposed research directions
\Cref{ch:Conclusion} concludes the thesis and outlines directions for
further research.