Abstract We introduce the random coupled-plaquette gauge model (RCPGM), which enables optimal accounting for Y -errors in decoding a surface code with noisy syndrome measurements. Using Parallel Tempering Monte Carlo simulations, we determine the code’s fundamental error thresholds. For phenomenological depolarizing data and bit-flip syndrome noise we determine a threshold of 6%, to be compared to the “uncoupled” random plaquette gauge model (RPGM) with a mere 4.3%. We then tackle the circuit-level noise scenario, where an approximate reduction technique allows us to exploit the RCPGM. Within the assumptions of the reduction technique, we find a threshold of 1.4%, to be compared to 0.7% when marginalizing Y -errors for the “uncoupled” RPGM. These results crucially enlarge the landscape of statistical mechanical mappings for quantum error correction. In particular they show further room for improvement of the surface code for fault-tolerant quantum computation and should be highly encouraging for practical decoder development.
Abstract We introduce the random coupled-plaquette gauge model (RCPGM), which enables optimal accounting for Y -errors in decoding a surface code with noisy syndrome measurements. Using Parallel Tempering Monte Carlo simulations, we determine the code’s fundamental error thresholds. For phenomenological depolarizing data and bit-flip syndrome noise we determine a threshold of 6%, to be compared to the “uncoupled” random plaquette gauge model (RPGM) with a mere 4.3%. We then tackle the circuit-level noise scenario, where an approximate reduction technique allows us to exploit the RCPGM. Within the assumptions of the reduction technique, we find a threshold of 1.4%, to be compared to 0.7% when marginalizing Y -errors for the “uncoupled” RPGM. These results crucially enlarge the landscape of statistical mechanical mappings for quantum error correction. In particular they show further room for improvement of the surface code for fault-tolerant quantum computation and should be highly encouraging for practical decoder development.
Z(2) lattice gauge theory plays an important role in the study of the threshold probability of Quantum Error Correction (QEC) for a quantum code. For certain QEC codes, such as the well-known Kitaev's toric/surface code, one can find a mapping of the QEC decoding problem onto a statistical mechanics model for a given noise model. The investigation of the threshold probability then corresponds to that of the phase diagram of the mapped statistical mechanics model. This can be studied by Monte Carlo simulation of the statistical mechanics model. In~\cite{Rispler}, we investigate the effects of realistic noise models on the toric/surface code in two dimensions together with syndrome measurement noise and introduce the random coupled-plaquette gauge model, 3-dimensional Z(2)×Z(2) lattice gauge theory. This new Z(2) gauge theory model captures main aspects of toric/surface code under depolarizing and syndrome noise. In these proceedings, we mainly focus on the aspects of Mont Carlo simulation and discuss preliminary results from Monte Carlo simulations of mapped classes of Z(2) lattice theories.
Noise in quantum computing is countered with quantum error correction. Achieving optimal performance will require tailoring codes and decoding algorithms to account for features of realistic noise, such as the common situation where the noise is biased towards dephasing. Here we introduce an efficient high-threshold decoder for a noise-tailored surface code based on minimum-weight perfect matching. The decoder exploits the symmetries of its syndrome under the action of biased noise and generalizes to the fault-tolerant regime where measurements are unreliable. Using this decoder, we obtain fault-tolerant thresholds in excess of 6% for a phenomenological noise model in the limit where dephasing dominates. These gains persist even for modest noise biases: we find a threshold of ∼5% in an experimentally relevant regime where dephasing errors occur at a rate 100 times greater than bit-flip errors.
Abstract Three-dimensional (3D) topological codes offer the advantage of supporting fault-tolerant implementations of non-Clifford gates, yet their performance against realistic noise remains largely unexplored. In this work, we focus on the paradigmatic 3D toric code and investigate its fault-tolerance thresholds in the presence of both Pauli and measurement errors. Two randomly coupled lattice gauge models that describe the code’s correctability are derived, including a random 2-form Z 2 gauge theory. By exploiting a generalized duality technique, we show that the 3D toric code exhibits optimal thresholds of p th X , M ≈ 11 % and p th Z , M ≈ 2 % against bit-flip and phase-flip errors, respectively. These threshold values show modest reductions compared to the case of perfect measurements, establishing the robustness of the 3D toric code against measurement errors. Our results constitute a substantial advance towards assessing the practical performance of 3D topological codes. This contribution is timely and in high demand, as rapid hardware advancements are bringing complex codes into experimental reach. Moreover, our work highlights the interdisciplinary nature of fault-tolerant quantum computation and holds significant interest for quantum information science, high-energy physics, and condensed matter physics.
Surface codes exploit topological protection to increase error resilience in quantum computing devices and can in principle be implemented in existing hardware. They are one of the most promising candidates for active error correction, not least due to a polynomial-time decoding algorithm which admits one of the highest predicted error thresholds. We consider the dependence of this threshold on underlying assumptions including different noise models, and analyze the performance of a minimum weight perfect matching (MWPM) decoding compared to a mathematically optimal maximum likelihood (ML) decoding. Our ML algorithm tracks the success probabilities for all possible corrections over time and accounts for individual gate failure probabilities and error propagation due to the syndrome measurement circuit. We present the very first evidence for the true error threshold of an optimal circuit level decoder, allowing us to draw conclusions about what kind of improvements are possible over standard MWPM.
We give a broad generalisation of the mapping, originally due to Dennis, Kitaev, Landahl, and Preskill, from quantum error correcting codes to statistical mechanical models. We show how the mapping can be extended to arbitrary stabiliser or subsystem codes subject to correlated Pauli noise models, including models of fault tolerance. This mapping connects the error correction threshold of the quantum code to a phase transition in the statistical mechanical model. Thus, any existing method for finding phase transitions, such as Monte Carlo simulations, can be applied to approximate the threshold of any such code, without having to perform optimal decoding. By way of example, we numerically study the threshold of the surface code under mildly correlated bit-flip noise, showing that noise with bunching correlations causes the threshold to drop to p_{\textup{corr}}=10.04(6)\% , from its known iid value of p_{\text{iid}}=10.917(3)\% . Complementing this, we show that the mapping also allows us to utilise any algorithm which can calculate/approximate partition functions of classical statistical mechanical models to perform optimal/approximately optimal decoding. Specifically, for 2D codes subject to locally correlated noise, we give a linear-time tensor network-based algorithm for approximate optimal decoding which extends the MPS decoder of Bravyi, Suchara and Vargo.
For quantum computers to reach their full potential will require error correction. We study the surface code, one of the most promising quantum error correcting codes, in the context of predominantly dephasing (Z-biased) noise, as found in many quantum architectures. We find that the surface code is highly resilient to Y-biased noise, and tailor it to Z-biased noise, whilst retaining its practical features. We demonstrate ultrahigh thresholds for the tailored surface code: ~39% with a realistic bias of = 100, and ~50% with pure Z noise, far exceeding known thresholds for the standard surface code: ~11% with pure Z noise, and ~19% with depolarizing noise. Furthermore, we provide strong evidence that the threshold of the tailored surface code tracks the hashing bound for all biases. We reveal the hidden structure of the tailored surface code with pure Z noise that is responsible for these ultrahigh thresholds. As a consequence, we prove that its threshold with pure Z noise is 50%, and we show that its distance to Z errors, and the number of failure modes, can be tuned by modifying its boundary. For codes with appropriately modified boundaries, the distance to Z errors is O(n) compared to O(n1/2) for square codes, where n is the number of physical qubits. We demonstrate that these characteristics yield a significant improvement in logical error rate with pure Z and Z-biased noise. Finally, we introduce an efficient approach to decoding that exploits code symmetries with respect to a given noise model, and extends readily to the fault-tolerant context, where measurements are unreliable. We use this approach to define a decoder for the tailored surface code with Z-biased noise. Although the decoder is suboptimal, we observe exceptionally high fault-tolerant thresholds of ~5% with bias = 100 and exceeding 6% with pure Z noise. Our results open up many avenues of research and, recent developments in bias-preserving gates, highlight their direct relevance to experiment.