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.

Description This artifact contains the core implementation of Kernpiler, a compiler framework for optimizing quantum circuits, and supports full reproducibility of all experimental results presented in the associated paper. The artifact includes all code, data pipelines, and scripts required to regenerate Figures 5–11. System Used for Data Collection NVIDIA A100 GPU with 80GB memory AMD EPYC 9654P 96-core processor x86_64 Linux system Python 3.13.5 Experiments may be computationally intensive, but they are fully parallelizable across multiple devices. Installation Clone or download the repository and navigate to the project directory. Create a virtual environment: python3 -m venv validate source validate/bin/activate Install dependencies: python -m pip install -r requirements.txt Install torch-scatter: python -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-2.10.0+cu128.html Experiment Workflow All experiment scripts are located in: src/compiler/optimization_passes/experiments Each figure can be reproduced by running its corresponding data collection and graphing scripts: Figure 5exp_gatecount_datacollection.py→ graph using:graph_data_scripts/graph_absolute.py Figure 6exp_partition_scaling_datacollection_o1exp_partition_scaling_datacollection→ graph using:graph_data_scripts/graph_o1_o2_side_by_side.py Figure 7exp_runtime_per_pass.py→ graph using:graph_data_scripts/graph_runtime_per_pass.py Figure 8exp_partition_scaling_datacollectiono1_phoenixFT→ graph using:graph_data_scripts/graph_firstorder_scalingFT.py Figure 9exp_partitionalgvsrandom.py→ graph using:graph_data_scripts/graph_partition_vs_random.py Figure 10exp_scaling_data_rewriteradius.py→ graph using:graph_data_scripts/graph_scalingdata.py Figure 11exp_error_scaling_systemsize.py→ output generated directly (no additional graph script required) Execution Notes All experiments are independent Parallel execution is supported Runtime varies depending on system size and hardware