Exact results on finite size corrections for surface codes tailored to biased noise

• URL: http://arxiv.org/abs/2401.04008v2
• Date: Thu, 5 Sep 2024 14:03:53 GMT
• Title: Exact results on finite size corrections for surface codes tailored to biased noise
• Authors: Yinzi Xiao, Basudha Srivastava, Mats Granath,
• Abstract summary: We study the XY and XZZX surface codes under phase-biased noise. We find exact solutions at a special disordered point. We show that calculating thresholds based not only on the total logical failure rate, but also independently on the phase- and bit-flip logical failure rates, gives a more confident estimate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The code-capacity threshold of a scalable quantum error correcting stabilizer code can be expressed as a thermodynamic phase transition of a corresponding random-bond Ising model. Here we study the XY and XZZX surface codes under phase-biased noise, $p_x=p_y=p_z/(2\eta)$, with $\eta\geq 1/2$, and total error rate $p=p_x+p_y+p_z$. By appropriately formulating the boundary conditions, in the rotated code geometry, we find exact solutions at a special disordered point, $p=\frac{1+\eta^{-1}}{2+\eta^{-1}}\gtrsim 0.5$, for arbitrary odd code distance $d$, where the codes reduce to one-dimensional Ising models. The total logical failure rate is given by $P_{f}=\frac{3}{4}-\frac{1}{4}e^{-2d_Z\,\text{artanh}(1/2\eta)}$, where $d_{Z}=d^2$ and $d$ for the two codes respectively, is the effective code distance for pure phase-flip noise. As a consequence, for code distances $d\ll \eta$, and error rates near the threshold, the XZZX code is effectively equivalent to the phase-flip correcting repetition code over $d$ qubits. The large finite size corrections for $d_Z<\eta$ also make threshold extractions, from numerical calculations at moderate code distances, unreliable. We show that calculating thresholds based not only on the total logical failure rate, but also independently on the phase- and bit-flip logical failure rates, gives a more confident estimate. Using this method for the XZZX code with a tensor-network based decoder and code distances up to $d\approx 100$, we find that the thresholds converge to a single value at moderate bias ($\eta=30, 100$), at an error rate above the hashing bound.

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