Averting multi-qubit burst errors in surface code magic state factories

TL;DR

This paper presents a novel, low-overhead software strategy for mitigating multi-qubit burst errors in surface code magic state factories, significantly reducing qubit-cycle costs in noisy quantum hardware.

Contribution

It introduces a method to detect and turn off affected parts of the factory upon error detection, avoiding the need to preserve logical information over time, thus improving efficiency.

Findings

Reduces qubit-cycle overhead by up to 13.9x

Effective in various noise regimes with precise physical noise models

Resilient to multiple simultaneous impact events

Abstract

Fault-tolerant quantum computation relies on the assumption of time-invariant, sufficiently low physical error rates. However, current superconducting quantum computers suffer from frequent disruptive noise events, including cosmic ray impacts and shifting two-level system defects. Several methods have been proposed to mitigate these issues in software, but they add large overheads in terms of physical qubit count, as it is difficult to preserve logical information through burst error events. We focus on mitigating multi-qubit burst errors in magic state factories, which are expected to comprise up to 95% of the space cost of future quantum programs. Our key insight is that magic state factories do not need to preserve logical information over time; once we detect an increase in local physical error rates, we can simply turn off parts of the factory that are affected, re-map the factory to the new chip geometry, and continue operating. This is much more efficient than previous more general methods, and is resilient even under many simultaneous impact events. Using precise physical noise models, we show an efficient ray detection method and evaluate our strategy in different noise regimes. Compared to existing baselines, we find reductions in ray-induced overheads by several orders of magnitude, reducing total qubitcycle cost by geomean 6.5x to 13.9x depending on the noise model. This work reduces the burden on hardware by providing low-overhead software mitigation of these errors.