Short Description
Parquet packaging of the Wigner-function data released for the Alice-Bob ETH Hackathon challenge. The original data have been converted into a columnar layout so that researchers can perform programmatic inspection, benchmarking, visualization, and ML workflows directly on canonical phase-space representations.
Scientific Background
A Wigner function encodes a quantum state as a quasi-probability distribution in the continuous-variable phase space, making it the standard diagnostic for bosonic modes and cat states. Positive and negative regions provide direct evidence of nonclassicality and allow reconstruction of parity-sensitive observables. This dataset mixes measured Wigner grids (experimental folder) with synthetic/noisy samples that were generated for the hackathon to test signal-processing pipelines. All data stems from the Alice & Bob / ETH challenge on bosonic quantum states and is meant purely for physics and ML experiments—no additional modeling has been applied beyond the original release.
Dataset Contents
- Each row corresponds to one Wigner function (one pickle).
source differentiates raw lab data (experimental) from generated baselines (synthetic).state_family identifies the class: cat states, Fock states, or noisy Wigner grids.state_variant specifies photon number, coherent amplitude, or synthetic noise index according to the filename.axis0 / axis1 store the phase-space coordinates, and wigner stores the 2D grid sampled on axis0 × axis1.- Numerical values are preserved exactly; only the storage format changed.
Schema
| Column | Type | Description |
|---|
source | String | Origin of the data: experimental or synthetic. |
state_family | String | Quantum state family (cat, fock, noisy_wigner). |
state_variant | String | Variant identifier (e.g., amplitude, photon number, noise index). |
axis0 | List(Float64) | First phase-space axis coordinates. |
axis1 | List(Float64) | Second phase-space axis coordinates. |
wigner | List(List(Float32)) | 2D Wigner function grid evaluated row-major on axis0 × axis1. |
axis0_len | Int16 | Length of axis0. |
axis1_len | Int16 | Length of axis1. |
wigner_min | Float32 | Minimum value of the Wigner function. |
wigner_max | Float32 | Maximum value of the Wigner function. |
Loading Examples
Polars
import polars as pl
from aqora_cli.pyarrow import dataset
df = pl.scan_pyarrow_dataset(
dataset("Alice_Bob/alice-and-bob-wigner-challenge", "v0.0.0")
).collect()
print(df.head())
Pandas
import pandas as pd
df = pd.read_parquet(
"aqora://Alice_Bob/alice-and-bob-wigner-challenge/v0.0.0"
)
print(df.head())
Example Usage Ideas
- Visualizing Wigner negativity or phase-space interference patterns.
- Training denoisers or generative models on bosonic-state data.
- Comparing experimental versus synthetic Wigner distributions for anomaly detection.
- Benchmarking quantum-state reconstruction or tomography pipelines.
Notes on Reproducibility
- No scientific transformations or recalibrations were applied to the underlying grids; only serialization changed from pickle to Parquet.
- All numerical values (axes, grids, extrema) are bitwise-identical to the originals.
- Aqora dataset versions are immutable; future releases will appear as new versions to preserve provenance.
Attribution
Keywords: Wigner function, continuous-variable quantum states, bosonic modes, cat states, quantum optics, phase-space representations.
