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shots
i64
failures
i64
task_id
str
core_seconds
f64
metadata
struct[5]
10,000,000
2,123
4,500.1582736969000
10,000,000
3,532
4,745.0896584987600
10,000,000
5,883
4,994.8015992641500
10,000,000
9,463
4,947.9806656837500
5,790,219
9,367
2,794.3926994800600
3,483,697
9,302
1,774.4859056472800
3,280,406
14,802
1,695.7095332145700
662,319
4,864
344.8405613899230
94,208
1,174
47.2330992221832
49,665
1,006
24.1332013607025
30 rows, 5 columns
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SELECT * FROM data
Fast 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)
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.
