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SELECT * FROM dataFast surgery for quantum LDPC codes
This dataset accompanies the paper Fast Surgery for Quantum LDPC Codes (2025) and consolidates the simulation results from all three CSV files provided by the authors on Zenodo (record 17220908). It provides empirical data used to benchmark the performance of lattice-surgery-based fault-tolerant operations for quantum LDPC codes under different noise configurations.Dataset contents
| Column | Type | Description |
|---|---|---|
shots | int64 | Number of Monte Carlo simulation shots performed |
failures | int64 | Number of failed logical operations during simulation |
task_id | string | Unique identifier for the simulation task |
core_seconds | float64 | Total compute time used for the simulation |
metadata | JSON | Configuration details of the simulation, including noise rate p, number of rounds, error sector, chosen decoder, and its hyperparameters (decoder_kwargs) |
metadata entry:
{
"p": 0.001,
"rounds": 3,
"sector": "Z",
"decoder": "bposd",
"decoder_kwargs": [
{
"max_iter": 100,
"ms_scaling_factor": 0.625,
"osd_method": "OSD_CS",
"osd_order": 15
}
]
}
Source
- Original paper: arXiv:2510.04521v1
- Original data: Zenodo record 17220908
- Blog Post: Iceberg-Quantum
Notes
All three CSVs published by the authors have been merged and converted to a unified Parquet format. The structure preserves the full fidelity of the original data while enabling efficient querying and analysis using libraries like Polars, PyArrow, or DuckDB.Quick start
# Install dependencies
pip install polars aqora-cli pyarrow fsspec
import polars as pl
from aqora_cli.pyarrow import dataset
# Load dataset
df = pl.scan_pyarrow_dataset(
dataset("aqora/fast-surgery-quantum-ldpc-codes", "v1.0.0")
)
df.head()