Most effective decoding algorithms for real-time surface-code error correction

Identify which decoding algorithms are most effective for real-time decoding of surface-code quantum error correction under superconducting-qubit constraints, meeting stringent latency and throughput requirements for feed-forward operations at relevant code distances, and quantify the performance–flexibility trade-offs for system design.

Background

Real-time feed-forward in surface-code-based fault-tolerant quantum computation requires decoders that can process syndromes within microsecond-scale latencies to avoid stalling logic-level operations, especially on fast superconducting platforms.

Multiple decoding approaches (e.g., MWPM, Union-Find, Fusion Blossom, neural decoders) exist, but their relative effectiveness under realistic hardware noise models, latency constraints, and large-scale parallelism remains unresolved; the paper explicitly states that the most effective algorithms are not yet known.