Here is a list of quantum codes that have notable decoders.

Name | Decoder(s) |
---|---|

2D color code | Projection decoder of \(O(n^4)\) complexity [1], modified to account for syndrome errors [2].Concatenated MPWM decoder [3]. |

2D hyperbolic surface code | Due to the symmetries of hyperbolic surface codes, optimal measurement schedules of the stabilizers can be found [4].Bounds on code capacity thresholds using ML decoding can be obtained by mapping the effect of noise on the code to a statistical mechanical model [5]. |

3D color code | Decoder that maps 3D color code to three copies of the 3D surface code [6]. |

3D surface code | Flip decoder and its modification p-flip [7].Tensor-network decoder [8].Efficient MWPM decoder for 3D toric and 3D welded surface codes [9].Generalization of linear-time ML erasure decoder [10] to 3D surface codes [9]. |

Abelian LP code | Ensemble BP decoder for codes without short cycles of length 4 [11]. |

Abelian quantum-double stabilizer code | Efficient decoder correcting below the code distance [12]. |

Analog stabilizer code | Homodyne measurement of nullifiers yields real-valued syndromes, and recovery can be performed by displacements conditional on the syndromes. |

Analog surface code | Shift-based decoder [13]. |

Approximate secret-sharing code | Decoding is analagous to reconstruction in a secret sharing scheme and is done in polynomial time. The only required operations are verification of quantum authentication, which is a pair of polynomial-time quantum algorithms that check if the fidelity of the received state is close to 1, and erasure correction for a stabilizer code, which involves solving a system of linear equations. |

Bacon-Shor code | Both Steane error correction and Shor error correction can be used for syndrome extraction, with the former outperforming the latter [14].Utilizing the mapping of the effect of the noise to a statistical mechanical model [15,16] yields several copies of the 1D Ising model [17; Sec. V.B].While check operators are few-body, stabilizer weights scale with the number of qubits, and stabilizer expectation values are obtained by taking products of gauge-operator expectation values. It is thus not clear how to extract stabilizer values in a fault-tolerant manner [18,19]. |

Balanced product (BP) code | BP-OSD decoder [20]. |

Binomial code | Photon loss and dephasing errors can be detected by measuring the phase-space rotation \(\exp\left(2\pi\mathrm{i} \hat{n} / (S+1)\right)\) and the check operator \(J_x/J\) in the spin-coherent state language, where \(J\) is the total angular momentum and \(J_x\) is the angular momentum in the \(x\) direction [21]. This type of error correction fails for errors that are products of photon loss/gain and dephasing errors. However, for certain \((N,S)\) instances of the binomial code, detection of these types of errors can be done.Recovery can be done via projective measurements and unitary operations [21,22].Fault-tolerant scheme that converts the required POVM into binary measurements whose redundancy is guaranteed by a classical code [23]. |

Bivariate bicycle (BB) code | Syndrome extraction circuit requires seven layers of CNOT gates regardless of code length. BP-OSD decoder [20] has been extended [24] to account for measurement errors (i.e., the circuit-based noise model [25]).Random and optimized syndrome extraction schedules from Ref. [24] are not distance preserving.Some long-range check operators can be measured less frequently than others [26]. |

Bosonic rotation code | One can distinguish (destructively) the codewords by performing a Fock-state number measurement. If a Fock state state \(|n\rangle\) is measured, then one rounds to the nearest integer of the form \((kq+j)/N\), and deduces that the true state was \(|\overline{j}\rangle\).One can distinguish states in the dual basis by performing phase estimation on \(\mathrm{e}^{\mathrm{i} \theta \hat n}\). One then rounds the resulting \(\theta\) to the nearest number \(2\pi j / qN\) in order to determine which dual basis state \(j \in \mathbb Z_q\) it came from.Autonomous quantum error correction schemes for \(S=1\) codes [27]. |

Camara-Ollivier-Tillich code | Iterative error estimation based on the MIN-SUM and SUM-PRODUCT algorithms [28]. |

Cat code | Measuring the Fock-state number modulo \(2S\) can be used to determine if photon loss or excitation errors occurred. For \(S=1\), this is the occupation number parity. |

Chamon model code | Repetition-based decoder, based on the three underlying repetition codes and improved by pre-treatment with a probabilistic greedy local algorithm [29]. |

Checkerboard model code | Parallelized matching decoder [30]. |

Chuang-Leung-Yamamoto (CLY) code | Destructive decoding with a photon number measurement on each mode.State can be decoded with a network of beamsplitters, phase shifters, and Kerr media. |

Cluster-state code | MBQC syndrome extraction is performed by multiplying certain single-qubit \(X\)-type measurements, which yield syndrome values. |

Codeword stabilized (CWS) code | There is no known efficient algorithm to decode non-additive (non-stabilizer) CWS codes.Clustered bounded-distance decoder [31–33].Structured error recovery [34], which reduces to syndrome-based recovery for additive (i.e., stabilizer) CWS codes. |

Color code | In contrast to the surface code, the color code can suffer from unremovable hook errors due to the specifics of its syndrome extraction circuits. Fault-tolerant decoders thus have to utilize additional flag qubits. |

Compass code | Asymmetrically-weighed variant of the union-find decoder [35]. |

Concatenated Steane code | There exist fault-tolerant syndrome extraction protocols for the concatenated Steane code [36].Randomized compiling helps reduce logical error rate for some noise models [37]. |

Concatenated bosonic code | Decoder exploiting analog information from the inner code for bosonic codes concatenated with qubit QLDPC codes [38]. |

Dinur-Hsieh-Lin-Vidick (DHLV) code | Linear-time decoder utilizing the small set-flip decoder [39] for \(Z\) errors and a reconstruction procedure for \(X\) errors [40]. |

Doubled color code | ML decoder that can utilize a history of syndromes, based on the Walsh-Hadamard transform [41]. |

EA qubit stabilizer code | Optical implementation of a minimal code using hyper-entangled states [42]. |

Eigenstate thermalization hypothesis (ETH) code | An explicit universal recovery channel for the ETH code is given in [43]. |

Expander LP code | Linear-time decoder [44].Logarithmic-time subroutine [45]. |

Fiber-bundle code | Greedy algorithm can be used to efficiently decode \(X\) errors, but no known efficient decoding of \(Z\) errors yet [18]. |

Fibonacci string-net code | Clustering decoder (provides best known threshold for this code) [46–48].Fusion-aware iterative minimum-weight perfect matching decoder. Note that ordinary MWPM decoders do not produce a threshold with this code [48].Cellular automaton decoder [49]. |

Five-qubit perfect code | Syndrome extraction circuit using only CNOT-SWAP gates [50].Combined dynamical decoupling and error correction protocol on individually-controlled qubits with always-on Ising couplings [51].Symmetric decoder correcting all weight-one Pauli errors. The resulting logical error channel after coherent noise has been explicitly derived [52]. |

Floquet color code | Period-six measurement sequence utilizing two-qubit measurements [53]. |

Folded quantum Reed-Solomon (FQRS) code | Quantum list decodable [54]. |

Fracton Floquet code | Period-six measurement sequence utilizing two-qubit measurements [53]. |

Frobenius code | Adapted from the Berlekamp decoding algorithm for classical BCH codes. There exists a polynomial time quantum algorithm to correct errors of weight at most \(\tau\), where \(\delta=2\tau+1\) is the BCH distance of the code [55]. |

GNU PI code | For a family of shifted gnu codes, decoding can be done using projection, probability amplitude rebalancing, and gate teleportation in time \(O(n^2)\) [56]. |

Galois-qudit code | For few-qudit codes (\(n\) is small), decoding can be based on a lookup table. For infinite code families, the size of such a table scales exponentially with \(n\), so approximate decoding algorithms scaling polynomially with \(n\) have to be used. The decoder determining the most likely error given a noise channel is called the maximum-likelihood (ML) decoder. |

Generalized 2D color code | Chromobius, an open-source implementation of the Mobius decoder works for many 2D color codes [57]. |

Generalized Shor code | Efficient decoder [58]. |

Generalized bicycle (GB) code | BP-OSD decoder [20]. |

Generalized five-squares code | Decoding of five-squares codes leads to a mapping of these codes to two copies of the surface code [59,60]. |

Gottesman-Kitaev-Preskill (GKP) code | The MLD decoder for Gaussian displacement errors is realized by evaluating a lattice theta function, and in general the decision can be approximated by either solving (approximating) the closest vector problem (CVP) [61] (a.k.a. closest lattice point problem) or by using other effective iterative schemes when, e.g., the lattice represents a concatenated GKP code [13,62–64]. While the decoder time scales exponentially with number of modes \(n\) generically, the time can be polynomial in \(n\) for certain codes [65].Babai's nearest plane algorithm [66] can be used for bounded-distance decoding [65]. |

Haah cubic code (CC) | Hard-decisions RG decoder [67]. |

Heavy-hexagon code | Any graph-based decoder can be used, such as MWPM and Union Find. However, edge weights must be dynamically renormalized using flag-qubit measurement outcomes after each syndrome measurement round.Machine-learning [68] and neural-network [69] decoders. |

Hierarchical code | Decoding is performed as in a standard concatenated code using a decoder for the inner code and outer code. The syndrome extraction circuit depth for the outer code is optimized using a permutation routing algorithm [70]. The bilayer architecture allows for logical entangling gates between logical surface-code patches.Soft output decoding [71]. |

High-dimensional expander (HDX) code | For 2D simplicial complexes, cycle code decoder admitting a polynomial-time decoding algorithm can be used [72]. |

Homological bosonic code | Decoding requires a different circuit for each possible erasure error, with no general circuit decoding any possible erasure error. Every circuit relies on a generalized conditional rotation, which Ref. [73] calls the QND Gate and which is defined as \(QND_c | x , y \rangle = |x + c y, y \rangle\). |

Homological code | Sweep decoder [74].Improved BP-OSD decoder [75].Renormalization-group (RG) decoder [76]. |

Homological product code | Union-find [77]. |

Honeycomb (6.6.6) color code | Message-passing decoder [78].Adaptation of the restriction decoder [79].Neural-network decoder [80].Mobius matching decoder gives low logical failure rate [81] and has an open-source implementation called Chromobius [57].AMBP4, a quaternary version [82] of the MBP decoder [83].MaxSAT-based decoder [84]. |

Honeycomb Floquet code | The ISG has a static subgroup for all time steps \(r\geq 3\) – that is, a subgroup which remains a subgroup of the ISG for all future times – given by so-called plaquette stabilizers. These are stabilizers consisting of products of check operators around homologically trivial paths. The syndrome bits correspond to the eigenvalues of the plaquette stabilizers. Because of the structure of the check operators, only one-third of all plaquettes are measured each round. The syndrome bits must therefore be represented by a lattice in spacetime, to reflect when and where the outcome was obtained. |

Hyperbolic Floquet code | Syndrome structure allows for MWPM decoding. |

Hyperbolic surface code | Hastings decoder [85]. |

Hypergraph product (HGP) code | ReShape decoder that uses minimum weight decoders for the classical codes used in the hypergraph construction [86].2D geometrically local syndrome extraction circuits with depth order \(O(\sqrt{n})\) using order \(O(n)\) ancilla qubits [87].Improved BP-OSD decoder [75].Erasure-correction can be implemented approximately with \(O(n^2)\) operations with quantum generalizations [88] of the peeling and pruned peeling decoders [89], with a probabilistic version running in \(O(n^{1.5})\) operations.Syndrome measurements are distance-preserving because syndrome extraction circuits can be designed to avoid hook errors [90].Generalization [91] of Viderman's algorithm for expander codes [92]. |

Kitaev surface code | Using data from multiple syndrome measurements prior to decoding allows for correcting syndrome measurement errors. The surface code requires order \(O(d)\) extraction rounds in order to gain a reliable estimate. Syndrome measurements are distance-preserving because syndrome extraction circuits can be designed to avoid hook errors [15].Expanding diamonds decoder correcting errors of some maximum fractal dimension [93]. The sub-threshold failure probability scales as \((p/p_{\text{th}})^{d^\beta}\), where \(p_{\text{th}}\) is the threshold and \(\beta = \log_3 2\).Minimum weight perfect-matching (MWPM) [15,94] (based on work by Edmonds on finding a matching in a graph [95,96]), which takes time up to polynomial in \(n\) for the surface code. For the case of the surface code, minimum-weight decoding reduces to MWPM [15,95,97]. MWPM solves the MPE decoding problem exactly for independent \(X\) and \(Z\) noise. MPE decoding is \(NP\)-hard in general for the surface code [98].Tensor network decoder [99] approximately solves the ML decoding problem under independent \(X,Z\) noise for the surface code and takes time of order \(O(n^2)\) [99]. ML decoding [15] is \(\#P\)-hard in general for the surface code [98].Union-find decoder [100] uses the union-find data structure [101–103], solving the MPE decoding problem exactly for low-weight errors under depolarizing noise. A subsequent modification utilizes the continuous signal obtained in the physical implementation of the stabilizer measurement (as opposed to discretizing the signal into a syndrome bit) [104]. Belief union find is a combination of belief-propagation and union-find [105]. Strictly local (as opposed to partially local) union find [106] has a worst-case runtime of order \(O(d^3)\) in the distance \(d\).Modified MWPM decoders: pipeline MWPM (accounting for correlations between events) [107,108]; modification tailored to asymmetric noise [109]; parity blossom MWPM and fusion blossom MWPM [110], a modification utilizing the continuous signal obtained in the physical implementation of the stabilizer measurement (as opposed to discretizing the signal into a syndrome bit) [104]; belief perfect matching (a combination of belief-propagation and MWPM) [105]; and spanning tree matching (STM) and rapid-fire (RFire) decoders [111]. Combinining, or harmonizing, various decoders can improve performance [112].Renormalization group (RG) [113–115]; see Ref. [116] for the planar surface code.Linear-time ML erasure decoder [10].Markov-chain Monte Carlo [117].Cellular automaton decoders [118–120]; see also [121].Neural network [122–124], reinforcement learning [125,126], and transformer-based [127] decoders.Lightweight low-latency look-up table (LILLIPUT) decoder for small surface codes [128].Decoders can be augmented with a pre-decoder [129,130], which can allow for some processing to be done inside the cryogenic environment of the quantum system [131].Sliding-window [132,133] and parallel-window [132] parallelizable decoders, designed to overcome the backlog problem, can be combined with many inner decoders, such as MWPM or union-find.Modifications of BP: generalized belief propagation (GBP) [134], based on a classical version [135]; AMBP4, a quaternary version [82] of the MBP decoder [83] of complexity \(O(n\log\log n)\); blockBP, a combination of BP and tensor-network decoders [136]. See Ref. [137] for a review of BP decoders.A color-code decoder can be used for the surface code [138].Progressive-Proximity Bit-Flipping (PPBF) decoder [139]. |

Low-depth random Clifford-circuit qubit code | Minimum-weight decoding via using tropical tensor networks [140]. |

Majorana box qubit | Qubit readout can be done by charge sensing [141–144]. |

Modular-qudit code | For few-qudit codes (\(n\) is small), decoding can be based on a lookup table. For infinite code families, the size of such a table scales exponentially with \(n\), so approximate decoding algorithms scaling polynomially with \(n\) have to be used. The decoder determining the most likely error given a noise channel is called the maximum-likelihood (ML) decoder. |

Modular-qudit stabilizer code | The structure of stabilizer codes allows for syndrome-based decoding, where errors are corrected based on the results of stabilizer measurements (syndromes).Trellis decoder for prime-dimensional qudits, which builds a compact representation of the algebraic structure of the normalizer \(\mathsf{N(S)}\) [145]. |

Modular-qudit surface code | Renormalization-group decoder [115,146]. |

NTRU-GKP code | Babai's nearest plane algorithm [66] can be used for bounded-distance decoding.An NTRU-based decoder against stochastic displacement noise is efficient because the decoding problem is equivalent to decrypting the NTRU cryptosystem with knowledge of the encoder. |

Number-phase code | Modular phase measurement done in the logical \(X\), or dual, basis has zero uncertainty in the case of ideal number phase codes. This is equivalent to a quantum measurement of the spectrum of the Susskind–Glogower phase operator. Approximate number-phase codes are characterized by vanishing phase uncertainty. Such measurements can be utilized for Knill error correction (a.k.a. telecorrection [147]), which is based on teleportation [148,149]. This type of error correction avoids the complicated correction procedures typical in Fock-state codes, but requires a supply of clean codewords [150]. Performance of this method was analyzed in Ref. [151].Number measurement can be done by extracting modular number information using a CROT gate \(\mathrm{e}^{(2\pi \mathrm{i} / NM) \hat n \otimes \hat n}\) and performing phase measurements [152,153] on an ancillary mode. See Section 4.B.1 of Ref. [150]. |

Oscillator-into-oscillator GKP code | Syndromes can be read off using ancilla modes, yielding partial information about noise in the logical modes that can then be used in an efficient ML decoding procedure [154]. |

PI qubit code | Schur-Weyl-transform based decoder [155]. Here, one first measures the total angular momentum of consecutive pairs of qubits, and then its projection modulo some spacing. Recovery can be performed by applying geometric phase gates [156] and the quantum Schur transform. |

Pair-cat code | Lindbladian-based dissipative encoding utilizing two-mode two-photon absorption [157]. Encoding passively protects against cavity dephasing, suppressing dephasing noise exponentially with \(\gamma^2\). |

Pastawski-Yoshida-Harlow-Preskill (HaPPY) code | Hierarchical recovery model [158].Greedy decoder [158]. |

Quantum Goppa code | Farran algorithm [159]. |

Quantum Tamo-Barg (QTB) code | Polynomially efficient decoder for QTB codes against errors acting on a number of subsystems that can go up to half of their conjectured distance [160; Thm. 8]. The decoder is based on decoding RS codes, and its runtime is independent of the locality \(r\).Polynomially efficient decoder for FQTB codes against errors acting on a number of subsystems that can go up to half of their conjectured distance [160; Thm. 7]. The runtime depends on the locality \(r\). |

Quantum Tanner code | Linear-time potetial-based decoder similar to the small-set-flip decoder for quantum expander codes [39].Linear-time decoder [44].Logarithmic-time mismatch decomposition decoder [45]. |

Quantum convolutional code | ML decoder [161]. |

Quantum data-syndrome (QDS) code | Syndrome errors are decoded using redundant stabilizer measurements. |

Quantum expander code | Small set-flip linear-time decoder, which corrects \(\Omega(n^{1/2})\) adversarial errors [162].Log-time decoder [163].Constant-time decoder [164].2D geometrically local syndrome extraction circuits acting on a patch of \(N\) physical qubits have to be of depth at least \(\Omega(n/\sqrt{N})\). More generally, there is a tradeoff between the depth \(D\) and width \(W\) of a syndrome extraction circuit, namely, \(D \geq n/\sqrt{W}\) [87]. |

Quantum polar code | Quantum successive-cancellation list decoder (SCL-E) for quantum polar codes that do not need entanglement assistance [165]. |

Quantum repetition code | Automaton-like decoders for the repetition code on a 2D lattice, otherwise known as the classical 2D Ising model, were developed by Toom [166,167]. An automaton by Gacs yields a decoder for a 1D lattice [168].Machine learning algorithm to implement continuous error-correction for the three-qubit quantum repetition code [169]. |

Quantum turbo code | Turbo decoder [170; Sec. V].Modified decoder yield improvement over the memoryless depolarizing channel [171]. |

Quantum-double code | For any solvable group \(G\), topological charge measurements can be done with an adaptive constant-depth circuit with geometrically local gates and measurements throughout [172]. |

Qubit CSS code | Coherent decoders allow for measurement-free error correction [173]. One method is table/multi-control decoding [174], which scales exponentially with the number of ancillas used in syndrome measurement. A fault-tolerant measurement-free scheme for low-distance CSS codes is formulated in Ref. [175]. Another method, the Ising-based decoder, utilizes the mapping of the effect of the noise to a statistical mechanical model [15,16] such that the decoding problem maps to preparation of the ground state of an Ising model.Decoders based on neural networks [176]. |

Qubit code | Incorporating faulty syndrome measurements can be done using the phenomenological noise model, which simulates errors during syndrome extraction by flipping some of the bits of the measured syndrome bit string. In the more involved circuit-level noise model, every component of the syndrome extraction circuit can be faulty.Hook errors are syndrome measurement circuit faults that cause more than one data-qubit error [15]. Hook errors occur at specific places in a syndrome extraction circuit and can sometimes be removed by re-ordering the gates of the circuit. If not, the use of flag qubits to detect hook errors may be necessary to yield fault-tolerant decoders.The decoder determining the most likely error given a noise channel is called the maximum probability error (MPE) decoder. For few-qubit codes (\(n\) is small), MPE decoding can be based by creating a lookup table. For infinite code families, the size of such a table scales exponentially with \(n\), so approximate decoding algorithms scaling polynomially with \(n\) have to be used.Decoders are characterized by an effective distance or circuit-level distance, the minimum number of faulty operations during syndrome measurement that is required to make an undetectable error. A code is distance-preserving if it admits a decoder whose circuit-level distance is equal to the code distance. |

Qubit stabilizer code | The structure of stabilizer codes allows for syndrome-based decoding, where errors are corrected based on the results of stabilizer measurements (syndromes). The size of the circuit extracting the syndrome depends on the weight of its corresponding stabilizer generator. Syndrome extraction circuits can be simulated efficiently using dedicated software (e.g., STIM [177]).MPE decoding, i.e., the process of finding the most likely error, is \(NP\)-complete in general [178,179]. If the noise model is such that the most likely error is the lowest-weight error, then ML decoding is called minimum-weight decoding. Maximum-likelihood (ML) decoding (a.k.a. degenerate maximum-likelihood decoding), i.e., the process of finding the most likely error class (up to degeneracy of errors), is \(\#P\)-complete in general [180].Incorporating faulty syndrome measurements can be done by performing spacetime decoding, i.e., using data from past rounds for decoding syndromes in any given round. If a decoder does not process syndrome data sufficiently quickly, it can lead to the backlog problem [181], slowing down the computation.Splitting decoders [182].Trellis decoder, which builds a compact representation of the algebraic structure of the normalizer \(\mathsf{N(S)}\) [183].Quantum extension of GRAND decoder [184].Deep neural-network probabilistic decoder [185].Generalized belief propagation (GBP) [134] based on a classical version [135].For codes encoding a single logical qubit, logical information can be extracted by single-qubit operations and classical communication [186].Correlated decoding can improve performance of Clifford and non-Clifford entangling gates [187]. |

Qudit-into-oscillator code | Given an encoding of a finite-dimensional code, a decoder that yields the optimal entanglement fidelity can be obtained by solving a semi-definite program [188,189] (see also Ref. [190]). This approximate QEC technique can be adapted to bosonic codes as long as they are restricted to a finite-dimensional subspace of the oscillator Hilbert space [21]. |

Raussendorf-Bravyi-Harrington (RBH) cluster-state code | MBQC syndrome extraction consists of single-qubit measurements and classical post-processing. The six \(X\)-measurements of qubits on the faces of a cube of the bcc lattice multiply to the product of the six cluster-state stabilizers whose vertices are on the faces of the cube. Such measurements, if done on a 2D slice, also yield \(Z\)-type syndromes on the next slice.Minimum weight perfect-matching (MWPM) [15,94] (based on work by Edmonds on finding a matching in a graph [95,96]). |

Rotated surface code | Only certain syndrome extraction schedules are distance-preserving [191].Local neural-network using 3D convolutions, combined with a separate global decoder [192]. |

Singleton-bound approaching AQECC | Quantum list decodable [54]. |

Spacetime circuit code | Efficient decoders can be constructed for some circuits [193]. |

Spin cat code | Measurement-free error correction protocol [194]. |

Square-lattice GKP code | Syndrome measurement can be done by applying a controlled-displacement controlled by an ancilla qubit. The syndrome information can be obtained by measuring the ancilla qubit after controlled-displacement opearation. See Section. 2D in [195].Decoder [196] based on Knill error correction (a.k.a. telecorrection [147]), which is based on teleportation [148,149].Pauli \(X\),\(Y\) and \(Z\) measurements can be performed by measuring \(-\hat{p},\hat{x}-\hat{p}\) and \(\hat{x}\) repectively. If the measurement outcome is closed to an even multiple of \(\sqrt{\pi}\), then the outcome is +1. If the measurement outcome is closed to an odd multiple of \(\sqrt{\pi}\), then the outcome is -1. See Section. 2D in [195].Reinforcement learning decoder that uses only one ancilla qubit [197]. It has been extended to utilize previously measured syndrome information [198]. |

Square-octagon (4.8.8) color code | Fault-tolerant syndrome extraction circuits [199].Matching decoder [200,201].Integer-program (IP) decoder [199].Two-copy surface-code decoder [202]. |

String-net code | Fusing non-Abelian anyons cannot be done in one step [203].Syndrome measurement circuits analyzed in Ref. [204].Clustering decoder [47]. |

Subsystem CSS code | Steane-type decoder utilizing data from the underlying classical codes [205]. |

Subsystem color code | Clustering decoder [206].Erasure decoder [207].Gauge-fixing decoders [207,208]. |

Subsystem hypergraph product (SHP) code | Efficient decoder [58]. |

Subsystem modular-qudit CSS code | Steane-type decoder utilizing data from the underlying classical codes [205]. |

Subsystem modular-qudit stabilizer code | Syndrome measurements are obtained by first measuring gauge operators of the code and taking their products, which give the stabilizer measurement outcomes. The order in which gauge operators are measured is important since they do not commute. There is a sufficient condition for inferring the stabilizer syndrome from the measurements of the gauge generators [59; Appendix].Decoder for certain geometrically local subsystem codes from hypergraphs [60]. |

Surface-17 code | Lookup table [191]. |

Three-qutrit code | The quantum information (the secret) can be recovered from a unitary transformation acting on only two qutrits, \( U_{ij} \otimes I \), where \(U_{ij}\) acts on qutrits \(i,j\) and \(I\) is the identity on the remaining qutrit. By the cyclic structure of the codewords, this unitary transformation performs a permutation that recovers the information and stores it in one of the two qutrits involved in recovery. |

Triangular surface code | The decoding uses a single decoding graph since the triangle code is not a CSS code. Nodes of the graph are located at each stabilizer (center of the triangle graph) and have red or blue edges, where red associates with \(X\) errors and blue with \(Z\) errors. To take into account any errors from measuring the error syndrome, a three-dimensional stack of the decoding graphs is laid on top of the code with vertical edges connecting to qubits between layers [209]. |

Twisted XZZX toric code | Fault-tolerant syndrome extraction circuits using flag qubits [210].AMBP4, a quaternary version [82] of the MBP decoder [83]. |

Two-component cat code | All-optical decoder [211] based on Knill error correction (a.k.a. telecorrection [147]), which is based on teleportation [148,149]. |

Union stabilizer (USt) code | Error-detection algorithm [31–33]. |

Very small logical qubit (VSLQ) code | Logical qubit can be measured with physical qubit measurements along \(X\). Can be implemented by engineering a coupling of one of the qubits to a readout cavity via the interaction \(\sigma_x (a+a^\dagger)\) [212]. This results in an \(X\)-dependent shift of the readout cavity resonance which can be measured.Star-code autonomous correction scheme [213]. |

Wasilewski-Banaszek code | Destructive measurement with photon number measurements on each mode. |

X-cube Floquet code | Period-six measurement sequence utilizing two-qubit measurements [214]. |

X-cube model code | Parallelized matching decoder [30]. |

XYZ color code | Efficient ML decoder at infinite bias [215].Cellular-automaton decoder [215]. |

XYZ\(^2\) hexagonal stabilizer code | Maximum-likelihood decoding using the EWD decoder [216]. |

XZZX surface code | MWPM decoder, which can be used for \(X\) and \(Z\) noise. For \(Y\) noise, a variant of the matching decoder could be used like it is used for the XY code in Ref. [217]. Decoding complexity scales as order \(O(n^3)\) because the code is non-CSS [82][217; Supplement]. |

Yoked surface code | Soft information from the inner surface codes can be utilized via a message passing algorithm [218].Yokes can be measured using lattice surgery [219]. |

\(((9,12,3))\) qubit code | Fault-tolerant scheme that converts the required POVM into 10 binary measurements whose redundancy is guaranteed by a classical code [23]. |

\([[12,2,4]]\) carbon code | Syndrome extraction circuit based on Knill error correction (a.k.a. telecorrection [147]), but using only two code blocks instead of three [220; Fig. 5]. |

\([[144,12,12]]\) gross code | The GDG sliding-window decoder [221], with a realization achieving a worst-case decoding latency of 3ms per window. |

\([[2^r-1, 2^r-2r-1, 3]]\) quantum Hamming code | Latin rectangle encoder [222].Efficient decoder [223]. |

\([[5,1,3]]_{\mathbb{R}}\) Braunstein five-mode code | Error correction can be done using linear-optical elements and feedback [224]. |

\([[6,4,2]]\) error-detecting code | Efficient decoder for the many-hypercube code [225]. |

\([[9,1,3]]\) Shor code | Bit- and phase-flip circuits utilize CNOT and Hadamard gates ([226], Fig. 10.6). |

\([[9,1,3]]_{\mathbb{R}}\) Lloyd-Slotine code | Syndromes are real-valued, and decoding is done by a continuous version of majority voting (a.k.a. triple modular redundancy). |

\(\chi^{(2)}\) code | Linear optics and \(\chi^{(2)}\) interactions. |

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