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    Layer Codes as Partially Self-Correcting Quantum Memories

    Shouzhen Gu1,*,†, Libor Caha2,*,‡, Shin Ho Choe2,3, Zhiyang He4, Aleksander Kubica1, and Eugene Tang5

    • *These authors contributed equally to this work.
    • †Contact author: shouzhen.gu@yale.edu
    • ‡Contact author: cahalibor@me.com

    Phys. Rev. Lett. 136, 210601 – Published 29 May, 2026

    DOI: https://doi.org/10.1103/x4z2-f4gg

    Abstract

    We investigate layer codes, a family of three-dimensional stabilizer codes that achieves optimal scaling of code parameters and energy barrier, as candidates for self-correcting quantum memories. First, we introduce two decoding algorithms for layer codes with provable guarantees for local stochastic and adversarial noise, respectively. We then prove that layer codes constitute partially self-correcting quantum memories which outperform previously analyzed models such as the cubic and welded solid codes. Notably, we argue that partial self-correction without the requirement of efficient decoding is more common than expected, as it arises solely from a diverging energy barrier. This draws a sharp distinction between partially self-correcting systems and partially self-correcting memories. Another novel aspect of our work is an analysis of layer codes constructed from random Calderbank-Shor-Steane codes. We show that these random layer codes have optimal scaling (up to logarithmic corrections) of code parameters and energy barrier. Finally, we present numerical studies of their memory times and report behavior consistent with partial self-correction.

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