AlphaSyndrome: Tackling the Syndrome Measurement Circuit Scheduling Problem for QEC Codes

TL;DR

AlphaSyndrome is an automated framework that optimizes syndrome-measurement circuit scheduling in quantum error correction codes, significantly reducing logical error rates by shaping error propagation.

Contribution

It introduces a novel optimization-based scheduling method using Monte Carlo Tree Search for general commuting-stabilizer codes, improving error correction performance.

Findings

Reduces logical error rates by up to 96.2%

Matches Google's surface-code schedules

Outperforms IBM's schedule for Bivariate Bicycle code

Abstract

Quantum error correction (QEC) is essential for scalable quantum computing, yet repeated syndrome-measurement cycles dominate its spacetime and hardware cost. Although stabilizers commute and admit many valid execution orders, different schedules induce distinct error-propagation paths under realistic noise, leading to large variations in logical error rate. Outside of surface codes, effective syndrome-measurement scheduling remains largely unexplored. We present AlphaSyndrome, an automated synthesis framework for scheduling syndrome-measurement circuits in general commuting-stabilizer codes under minimal assumptions: mutually commuting stabilizers and a heuristic decoder. AlphaSyndrome formulates scheduling as an optimization problem that shapes error propagation to (i) avoid patterns close to logical operators and (ii) remain within the decoder's correctable region. The framework uses Monte Carlo Tree Search (MCTS) to explore ordering and parallelism, guided by code structure and decoder feedback. Across diverse code families, sizes, and decoders, AlphaSyndrome reduces logical error rates by 80.6% on average (up to 96.2%) relative to depth-optimal baselines, matches Google's hand-crafted surface-code schedules, and outperforms IBM's schedule for the Bivariate Bicycle code.