aiqbench analysis
This research presents a significant theoretical advance for fault-tolerant quantum computing by proving that minimum-weight decoding of 2D topological codes admits a polynomial-time approximation scheme. By adapting algorithms from Euclidean geometry, the study offers an efficient method to find near-optimal recovery operators even when exact solutions are computationally intractable (NP-hard). This development is crucial for practical implementations involving toric and color codes under realistic noise models across various dimensions.
A polynomial-time approximation scheme for minimum-weight decoding of topological codes
arXiv:2606.18145v1 Announce Type: new Abstract: 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 $\varepsilon>0$, a recovery operator of weight within a multiplicative factor of $1+\varepsilon$ 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.
Abstract: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 $\varepsilon>0$, a recovery operator of weight within a multiplicative factor of $1+\varepsilon$ 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.
Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS)
arXiv:2606.18145 [quant-ph]
(or arXiv:2606.18145v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.18145 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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