page_size to embed more in the static export.SELECT * FROM datan = 4, 8, 16, 32)v and their binary labels x_v are streamed into a compact quantum sketch.(Π⁺, Π⁻, Π₀) recorded in measuredResults.
The dataset also tracks the measurement branch (meas) so fidelity calculations can focus on the meas = [1, 0] subset, and includes auxiliary observables (a, b) used internally for vertex and edge updates.| Column | Type | Description |
|---|---|---|
n | Integer | Hidden Matching problem size (4, 8, 16, or 32). |
vertex | ListInt | Streamed vertex sequence for the shot. |
vertex_bit | ListInt | Binary labels assigned to streamed vertices. |
edge_1 | ListInt | First endpoints of the streamed matching edges. |
edge_2 | ListInt | Second endpoints of the streamed matching edges. |
measuredResults | ListInt | Projective measurement result for the final sketch. |
meas | ListInt | Measurement-branch indicator ([1, 0] marks shots used for fidelity estimates). |
a | ListInt | Auxiliary observable for vertex-update tracking (nullable). |
b | ListInt | Auxiliary observable for edge-update tracking (nullable). |
null.
import polars as pl
from aqora_cli.pyarrow import dataset
df = pl.scan_pyarrow_dataset(
dataset("aqora/realization-of-a-quantum-streaming-algorithm-on-long-lived-trapped-ion-qubits", "v0.1.0")
).collect()
stats = (
df.groupby("n")
.agg([
pl.len().alias("shots"),
pl.col("measuredResults").list.last().mean().alias("avg_last_bit"),
])
.sort("n")
)
print(stats)
import pandas as pd
df = pd.read_parquet("aqora://aqora/realization-of-a-quantum-streaming-algorithm-on-long-lived-trapped-ion-qubits/v0.1.0")
print(df.head())
meas == [1, 0] and comparing measuredResults against the HM ground truth.n to study how classical sketches diverge from the experiment.a, b) and sketch fidelity to identify hardware drift.
For full experimental details—circuit construction, streaming protocol, and theoretical guarantees—consult arXiv:2511.03689. This README serves as the Aqora dataset card for the shot-level Parquet artifact.