Two-dimensional topological translationally-invariant (TTI) quantum codes, such as the toric code (TC) and bivariate bicycle (BB) codes, are promising candidates for fault-tolerant quantum computation. For such codes to be practically relevant, their decoders must successfully correct the most likely errors while remaining computationally efficient. For the TC, graph-matching decoders satisfy both requirements and, additionally, admit provable performance guarantees. Given the equivalence between TTI codes and (multiple copies of) the TC, one may then ask whether TTI codes also admit analogous graph-matching decoders. In this work, we develop a graph-matching approach to decoding general TTI codes. Intuitively, our approach coarse-grains the TTI code to obtain an effective description of the syndrome in terms of TC excitations, which can then be removed using graph-matching techniques. We prove that our decoders correct errors of weight up to a constant fraction of the code distance and achieve nonzero code-capacity and circuit-level thresholds in the specified noise models. We further numerically study a variant optimized for practically relevant BB codes and observe performance comparable to that of belief propagation with ordered statistics decoding (BP-OSD). We also study the code-capacity threshold for the hexagonal color code as a proof of concept. Our results indicate that graph-matching decoders are a viable approach to decoding BB codes and other TTI codes.
Two-dimensional topological translationally-invariant (TTI) quantum codes, such as the toric code (TC) and bivariate bicycle (BB) codes, are promising candidates for fault-tolerant quantum computation. For such codes to be practically relevant, their decoders must successfully correct the most likely errors while remaining computationally efficient. For the TC, graph-matching decoders satisfy both requirements and, additionally, admit provable performance guarantees. Given the equivalence between TTI codes and (multiple copies of) the TC, one may then ask whether TTI codes also admit analogous graph-matching decoders. In this work, we develop a graph-matching approach to decoding general TTI codes. Intuitively, our approach coarse-grains the TTI code to obtain an effective description of the syndrome in terms of TC excitations, which can then be removed using graph-matching techniques. We prove that our decoders correct errors of weight up to a constant fraction of the code distance and achieve nonzero code-capacity and circuit-level thresholds in the specified noise models. We further numerically study a variant optimized for practically relevant BB codes and observe performance comparable to that of belief propagation with ordered statistics decoding (BP-OSD). We also study the code-capacity threshold for the hexagonal color code as a proof of concept. Our results indicate that graph-matching decoders are a viable approach to decoding BB codes and other TTI codes.
Two-dimensional topological translationally invariant (2D TTI) stabilizer codes lie at the heart of fault-tolerant quantum computation, but using them requires solving the decoding problem. Minimum-weight decoding of these codes was recently shown to be NP-hard, even in basic settings, such as the color code with Pauli Z errors and the toric code with Pauli X, Y and Z errors. Here, we prove that minimum-weight decoding of 2D TTI codes nonetheless admits a polynomial-time approximation scheme (PTAS), i.e., for any constant ε>0, a recovery operator of weight within a multiplicative factor of 1+ε of the minimum can be found in polynomial time. Our approach builds on Arora's PTAS for Euclidean problems, such as the traveling salesman problem, and applies when decoding can be cast in terms of point-like excitations connected by string-like errors. It therefore extends beyond two dimensions, covering certain higher-dimensional topological codes and quantum memories, including the toric code with phenomenological or circuit-level noise.
The discovery of new quantum error-correcting codes that encode several logical qubits into relatively few physical qubits motivates the development of efficient and accurate methods of decoding these systems. Here, we adopt the minimum-weight perfect matching algorithm, a subroutine invaluable to decoding topological codes, to decode bivariate bicycle codes. Using the equivalence of bivariate bicycle codes to copies of the toric code, we propose a method we call the `cylinder trick' to rapidly find a correction using matching on code symmetries. We benchmark our decoder on the gross code family, cyclic hypergraph-product codes, generalized toric codes, and recently proposed directional codes under code capacity and phenomenological noise models, demonstrating the general applicability of our protocol. For a subset of these codes, we find that our decoder can be significantly improved by augmenting matching with strategies including belief propagation and `over-matching', thus achieving performance competitive with state-of-the-art approaches.
There has been significant recent interest in near-term qLDPC codes as high-performance alternatives to surface and color codes. One such class of codes is 2-dimensional translationally invariant (TI) codes, such as bivariate bicycle codes, which share similar properties to topological codes. Fundamental objects in the study of such codes are the materialised symmetries, which can be used for the construction of matching-based decoders. These decoders generalise the minimum-weight perfect matching decoder for toric codes and provide similar asymptotic performance guarantees that heuristic decoders such as BP and its variants lack. Despite this, the mathematical structure of symmetries along with their properties under translation and restriction to finite-sized lattices has not been well-studied. We describe a decomposition of symmetry spaces of 2D CSS TI codes on infinite lattices into translation-invariant subspaces which allow us to write an explicit basis of symmetries in a plane-wave-like form. We then describe how to adapt this infinite lattice basis so that any corresponding rectangular periodic lattice basis can be obtained by restriction to basis elements compatible with the periodic boundaries. This allows us to describe how the symmetry space varies with different rectangular dimensions. We illustrate via examples that it is often straightforward in practice to determine a basis of symmetries on twisted boundaries as well, without needing to recompute the decomposition. We comment on the applications of this framework to matching-based decoders and provide examples of symmetries of such codes as the gross code.
Quantum error correction (QEC) is critical for scalable fault-tolerant quantum computing. Topological codes, such as the toric code, offer hardware-efficient architectures but their Tanner graphs contain many girth-4 cycles that degrade the performance of belief-propagation (BP) decoding. For this reason, BP decoding is typically followed by a more complex second stage decoder such as minimum-weight perfect matching. These combined decoders achieve a remarkable performance, albeit at the cost of increased complexity. In this paper we propose two key improvements for the decoding of toric code. The first one is replacing the BP decoder by a neural BP decoder, giving rise to the neural belief-matching decoder which substantially decreases the average decoding complexity. The main drawback of this approach is the high cost associated with the training of the neural BP decoder. To address this issue, we impose a convolutional architecture on the neural BP decoder, enabling weight sharing across the spatially homogeneous structure of the code's factor graph. This design allows a model trained on a modest-size topological code to be directly transferred to much larger instances, preserving decoding quality while dramatically lowering the training burden. Our numerical experiments on toric-code lattices of various sizes demonstrate that this technique does not result in a noticeable loss in performance.
Fair threshold estimation for bivariate bicycle (BB) codes on the quantum erasure channel runs into two recurring problems: decoder-baseline unfairness and the conflation of finite-size pseudo-thresholds with true asymptotic thresholds. We run both uninformed and \emph{erasure-aware} minimum-weight perfect matching (MWPM) toric code baselines alongside BP-OSD decoding of BB codes. With standard depolarizing-weight MWPM and no erasure information, performance matches random guessing on the erasure channel in our tested regime -- so prior work that compares against this baseline is really comparing decoders, not codes. Using 200{,}000 shots per point and bootstrap confidence intervals, we sweep five BB code sizes from N=144 to N=1296. Pseudo-thresholds (WER = 0.10) run from p∗=0.370 to 0.471; finite-size scaling (FSS) gives an asymptotic threshold p∞∗≈0.488, within 2.4\% of the zero-rate limit and without maximum-likelihood decoding. On the fair baseline, BB at N=1296 has a modest edge in threshold over the toric code at twice the qubit count, and a 12× lower normalized overhead -- the latter is where the practical advantage sits. All runs are reproducible from recorded seeds and package versions.
In the quest for fault-tolerant quantum computation, the delicate nature of qubits demands the need for robust quantum error correction. Quantum error correcting codes, the quantum counterpart to classical error correcting codes in information theory, have been developed to address the unique challenges quantum bits face against the noise of their environment in storing information. These codes include but are not limited to the Kitaev surface code, toric code, rotated surface code, generalized bicycle code, and bivariate bicycle code. Fast and reliable decoders for these quantum error correcting codes are needed to correct errors and achieve fault tolerance on these qubits. The belief propagation algorithm, which is a efficient and reliable heuristic for decoding classical low-density parity check codes, has been generalized to different types of quantum error correction codes. Paired with the ordered statistics decoding algorithm as a post-processing step from the soft outputs of the belief propagation decoder, belief propagation and ordered statistics decoding has been one of the most promising avenues of efficient quantum error correction. This thesis combines the state-of-the-art belief propagation and ordered statistics decoding algorithms with the exploration of the neural belief propagation algorithm to leverage classical deep learning machines to learn message passing patterns in decoding the newest quantum codes, the rotated surface code and bivariate bicycle code. The neural network exploits the local lattice structure of these codes to adjust the trainable weights and biases to more learn message update patterns more efficiently than the vanilla belief propagation algorithm. In this thesis, we prove a new threshold for the physical error rate on the rotated surface code using a neural belief propagation algorithm and ordered statistics decoding that consistently outperforms the threshold established by the vanilla belief propagation algorithm with ordered statistics decoding. We additionally show the results for similar efforts on the bivariate bicycle codes, and discuss the difficulties in achieving a threshold using the current neural belief propagation algorithms.