Efficient Algorithms for Maximum Likelihood Decoding in the Surface Code

TL;DR

This paper introduces efficient algorithms for maximum likelihood decoding of the 2D surface code, including exact and approximate methods that improve error correction performance under various noise models.

Contribution

It presents a novel reduction of MLD to matchgate circuit simulation for exact decoding and an MPS-based approximate decoding method for general noise models.

Findings

Exact MLD runs in $O(n^2)$ time for specific noise models.

Approximate MLD with MPS achieves significant error reduction.

Benchmark shows improved logical error rates over standard decoders.

Abstract

We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the 2D surface code with a noiseless syndrome extraction. First, we show how to implement MLD exactly in time $O(n^2)$, where $n$ is the number of code qubits. Our implementation uses a reduction from MLD to simulation of matchgate quantum circuits. This reduction however requires a special noise model with independent bit-flip and phase-flip errors. Secondly, we show how to implement MLD approximately for more general noise models using matrix product states (MPS). Our implementation has running time $O(n\chi^3)$ where $\chi$ is a parameter that controls the approximation precision. The key step of our algorithm, borrowed from the DMRG method, is a subroutine for contracting a tensor network on the two-dimensional grid. The subroutine uses MPS with a bond dimension $\chi$ to approximate the sequence of tensors arising in the course of contraction. We benchmark the MPS-based decoder against the standard minimum weight matching decoder observing a significant reduction of the logical error probability for $\chi\ge 4$.