The rapidly growing landscape of quantum error-correction (QEC) protocols has produced a wealth of numerical data, but comparatively few heuristics for understanding and predicting their performance. Here, we develop a simple entropy-based proxy that predicts the thresholds of a variety of QEC protocols, ranging from the code-capacity setting of Clifford-deformed surface codes with biased Pauli or erasure noise to the circuit-level noise model of the surface or color codes with flag qubits. Our proxy estimates the threshold by locally comparing the noise entropy with the error information gained via stabilizer measurements (or spacetime detectors and flag outcomes in the circuit-level settings). Despite neglecting correlations between stabilizer outcomes and the contribution from code degeneracy, the proxy captures the main trends across diverse settings and yields threshold estimates in good agreement with numerical results. Our work develops much-needed phenomenology that enables simple back-of-the-envelope estimates of QEC performance, recovering and providing an explanation for the results of computationally intensive detailed simulations.
The rapidly growing landscape of quantum error-correction (QEC) protocols has produced a wealth of numerical data, but comparatively few heuristics for understanding and predicting their performance. Here, we develop a simple entropy-based proxy that predicts the thresholds of a variety of QEC protocols, ranging from the code-capacity setting of Clifford-deformed surface codes with biased Pauli or erasure noise to the circuit-level noise model of the surface or color codes with flag qubits. Our proxy estimates the threshold by locally comparing the noise entropy with the error information gained via stabilizer measurements (or spacetime detectors and flag outcomes in the circuit-level settings). Despite neglecting correlations between stabilizer outcomes and the contribution from code degeneracy, the proxy captures the main trends across diverse settings and yields threshold estimates in good agreement with numerical results. Our work develops much-needed phenomenology that enables simple back-of-the-envelope estimates of QEC performance, recovering and providing an explanation for the results of computationally intensive detailed simulations.
Quantum error correcting (QEC) codes protect quantum information from decoherence as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, suboptimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered, we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close agreement with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models. Published by the American Physical Society 2024
Quantum error correcting (QEC) codes protect quantum information from decoherence, as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, sub-optimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close accordance with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive practical tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models.
As quantum computers remain susceptible to noise, QEC is essential for preserving logical information during computations. However, the performance of QEC codes breaks down beyond certain noise thresholds, revealing fundamental limits on their ability to protect quantum information. These limits can be characterized using information-theoretic measures such as the coherent information, which quantifies the maximum rate at which logical information can be reliably transmitted through a noisy quantum channel. In this work, we establish a direct connection between the CI and the binary cross-entropy loss used when training neural network decoders. Specifically, we show that the CI constitutes a sharp lower bound on the achievable loss for decoders that track logical operators across noisy channels. To this end, we develop a transformer-based neural network model based on maximum likelihood decoding. We train this network to estimate the CI and evaluate its performance on the surface code under three noise models: code capacity, phenomenological, and circuit-level noise. Our results demonstrate that the network accurately predicts CI and yields threshold estimates that closely match known theoretical limits. When used as a decoder, the network significantly outperforms the minimum weight perfect matching decoder in terms of logical error rate. We also introduce a novel soft post-selection scheme that independently treats uncertainty in both logical operators and relies on confidence-based filtering of the network's output. We prove that such post-selection strategies, based on the MLD cosets, are optimal, and demonstrate their scalability in terms of both logical error rate and abort probability. These findings establish transformer-based architectures as powerful tools for QEC and provide the first numerical evidence supporting the optimality and scalability of MLD-based post-selection.
This work proposes a simple and interpretable engineering framework for estimating operational thresholds in quantum error correction (QEC) systems. Rather than introducing new physical principles or modifying the formal structure of quantum mechanics, the framework reformulates existing concepts from quantum error correction, decoherence modeling, and information indicators into a compact phenomenological model suitable for device calibration and benchmarking. The central element of the approach is an entropy-related scalar indicator that acts as a proxy for accumulated error information. A simple threshold relation connects this indicator with code distance and the measured quantum bit error rate (QBER), allowing effective thresholds to be estimated using a fixed-parameter batch calibration method. The framework is intentionally designed as a lightweight engineering tool. It avoids adaptive learning, online control, and foundational claims regarding quantum measurement or wavefunction collapse. Instead, it focuses on practical usability, interpretability, and low computational cost. Proof-of-concept numerical demonstrations show that parameter estimation converges reliably and that the model provides stable threshold estimates from limited datasets. This approach may be useful for rapid calibration, exploratory benchmarking, and comparative analysis in near-term quantum error correction experiments.
Color codes are promising quantum error-correction (QEC) codes because they have an advantage over surface codes in that all Clifford gates can be implemented transversally. However, the thresholds of color codes under circuit-level noise are relatively low, mainly because measurements of their high-weight stabilizer generators cause an increase in the circuit depth and, thus, substantial errors are introduced. This makes color codes not the best candidate for fault-tolerant quantum computing. Here, we propose a method to suppress the impact of such errors by optimizing weights of decoders using conditional error probabilities conditioned on the measurement outcomes of flag qubits. In numerical simulations, we improve the threshold of the (4.8.8) color code under circuit-level noise from 0.14% to around 0.27%, which is calculated by using an integer programming decoder. Furthermore, in the (6.6.6) color code, we achieve a circuit-level threshold of around 0.36%, which is almost the same value as the highest value in the previous studies employing the same noise model. In both cases, the effective code distance is also improved compared to a conventional method that uses a single ancilla qubit for each stabilizer measurement. Thereby, the achieved logical error rates at low physical error rates are almost one order of magnitude lower than those of the conventional method with the same code distance. Even when compared to the single-ancilla method with a higher code distance, considering the increased number of qubits used in our method, we achieve lower logical error rates in most cases. This method can also be applied to other weight-based decoders, making the color codes more promising as candidates for the experimental implementation of QEC. Furthermore, one can utilize this approach to improve a threshold of wider classes of QEC codes, such as high-rate quantum low-density parity-check codes. Published by the American Physical Society 2024
A quantum error correction code is assessed over its ability to correct errors in noisy quantum circuits. This task requires extensive simulations of faulty quantum circuits, which are often made tractable by considering stochastic Pauli noise models, as they are compatible with efficient classical simulation techniques. However, such noise models do not fully capture the variety of physical error mechanisms encountered in realistic quantum platforms. In this work, we extend circuit-level noise modeling beyond the Pauli framework by estimating the threshold of the hexagonal color code under more general noise models. Specifically, we consider two representative non-Pauli error channels: a systematic X-rotation model that introduces coherent over-rotations, and an amplitude damping channel that captures relaxation processes. These models are incorporated at the circuit level into color code circuits using a Tree Tensor Network ansatz. Our simulations demonstrate that tensor network simulations enable accurate threshold estimation under non-Pauli noise for color codes up to distance d=7 (73 qubits). Comparing our results with the Pauli twirling approximations of the noise models, we find that coherent over-rotations yield systematically higher error rates, deviating from the Pauli twirling approximation as the code distance increases.