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SELECT * FROM dataHyperoptimized Quantum LEGO Contraction Schedules
This dataset aggregates contraction-schedule results for Quantum LEGO (QL) layouts used to compute weight enumerator polynomials (WEPs) of stabilizer codes. It accompanies the paper Hyper-optimized Quantum LEGO Contraction Schedules (2025), which introduces the Sparse Stabilizer Tensor (SST) cost function and benchmarks hyper-optimized schedules (via cotengra) across QL layouts such as MSP and Tanner. The dataset combines the authors’ MSP/Tanner layout runs; see the paper’s Sec. III.4 and VI for layout details.Quick start
# Install: pip install polars aqora-cli pyarrow fsspec
import polars as pl
from aqora_cli.pyarrow import dataset
df = pl.scan_pyarrow_dataset(
dataset("aqora/hyperoptimized-quantum-lego-contraction-schedules", "v1.1.1")
)
# Peek schema without reading all data:
print(df.collect_schema()) # shows column names/types
# Example 1 — compare average ops by cost function for MSP layouts
res1 = (
df.filter(pl.col("representation") == "MSP")
.group_by("cost_fn")
.agg(pl.col("operations").mean().alias("avg_ops"))
.sort("avg_ops")
.collect()
)
print(res1)
# Example 2 — scaling: max intermediate tensor by distance
res2 = (
df.group_by("distance")
.agg(pl.col("max tensor size").max().alias("max_intermediate"))
.sort("distance")
.collect()
)
print(res2)
# Example 3 — when does QL beat brute force?
res3 = (
df.filter(pl.col("operations_w_bruteforce").is_not_null())
.with_columns((pl.col("operations") < pl.col("operations_w_bruteforce"))
.alias("ql_beats_bruteforce"))
.group_by("representation")
.agg(pl.col("ql_beats_bruteforce").mean().alias("share_better"))
.sort("share_better", descending=True)
.collect()
)
print(res3)
Schema
All columns are preserved as in the source CSVs.| Column | Type | Description |
|---|---|---|
cost_fn | String | Cost function used when searching schedules (e.g., SST vs default/dense). |
representation | String | QL layout / graph representation (e.g., MSP, Tanner, surface, concatenated; values depend on source). |
num_run_x | Integer | Run/repeat counter (first pass or phase) as exported by authors’ scripts. |
distance | Integer | Code distance or layout parameter where applicable. |
num_qubits | Integer | Number of physical qubits in the instance. |
contraction_time | Float | Wall-clock or measured time for the recorded contraction run (seconds, as logged). |
contraction cost | Float | Reported contraction cost metric for the selected path (definition follows cost_fn; see Notes). |
contraction width | Float | Reported path width / max intermediate size metric for the schedule (per source export). |
operations | Float | Estimated floating-point operations for the contraction (per cotengra export). |
avg intermediate tensor | Float | Average intermediate tensor size during contraction (source export). |
max tensor size | Float | Maximum intermediate tensor size during contraction (source export). |
wep | String | WEP target label for the instance (e.g., scalar vs tensor WEP variants; see paper background). |
score_cotengra | Float | cotengra’s internal objective/score for the chosen path. |
brute_force | Float | Baseline metric exported by the authors for brute-force computation. |
num_run_y | Float | Secondary run/repeat counter (second pass or phase). |
brute_force_operations | Float | Estimated operations for brute-force evaluation (if provided). |
operations_w_bruteforce | Float | Combined/alternative operations estimate including brute-force baseline (if provided). |
Notes on cost functions
The paper shows that intermediate tensors in stabilizer-WEP QL networks are often highly sparse, making dense-assumption cost functions unreliable. The SST cost, derived from parity-check matrix ranks, correlates tightly with true cost and improves schedule selection. Expectcontraction cost to reflect the chosen cost_fn semantics.
Caveats
score_cotengra,contraction cost, andoperationsare model-/tool-dependent estimates; interpret comparatively, not as absolute FLOPs guarantees.- Sparse vs dense assumptions matter; see the paper’s discussion of SST vs default cost.