Hardness results for decoding the surface code with Pauli noise

TL;DR

This paper proves that decoding the surface code with detailed qubit-dependent Pauli noise models is computationally hard (NP-hard and #P-hard), highlighting fundamental limits on optimal decoding algorithms.

Contribution

It establishes the NP-hardness and #P-hardness of maximum likelihood decoding for surface codes with realistic noise models, extending known complexity results.

Findings

Decoding with detailed noise models is NP-hard.

Decoding with detailed noise models is #P-hard.

Hardness of approximation results are provided.

Abstract

Real quantum computers will be subject to complicated, qubit-dependent noise, instead of simple noise such as depolarizing noise with the same strength for all qubits. We can do quantum error correction more effectively if our decoding algorithms take into account this prior information about the specific noise present. This motivates us to consider the complexity of surface code decoding where the input to the decoding problem is not only the syndrome-measurement results, but also a noise model in the form of probabilities of single-qubit Pauli errors for every qubit. In this setting, we show that quantum maximum likelihood decoding (QMLD) and degenerate quantum maximum likelihood decoding (DQMLD) for the surface code are NP-hard and #P-hard, respectively. We reduce directly from SAT for QMLD, and from #SAT for DQMLD, by showing how to transform a boolean formula into a qubit-dependent Pauli noise model and set of syndromes that encode the satisfiability properties of the formula. We also give hardness of approximation results for QMLD and DQMLD. These are worst-case hardness results that do not contradict the empirical fact that many efficient surface code decoders are correct in the average case (i.e., for most sets of syndromes and for most reasonable noise models). These hardness results are nicely analogous with the known hardness results for QMLD and DQMLD for arbitrary stabilizer codes with independent $X$ and $Z$ noise.