---
license: apache-2.0
task_categories:
  - other
tags:
  - quantum-error-correction
  - surface-code
  - willow
pretty_name: Willow Surface-Code Detection Events
configs:
  - config_name: default
    data_files: data/*.parquet
---

# Willow Surface-Code Detection Events (ingested)

Detection events and logical-observable flips derived from Google's Willow
below-threshold surface-code dataset (Zenodo
[10.5281/zenodo.13273331](https://doi.org/10.5281/zenodo.13273331)), rotated
surface code at distances 3, 5, 7 in X and Z memory.

Each row is one experimental **shot**. Detection events are derived from the raw
device measurement records with Stim's measurement-to-detector converter, using
the per-shot sweep bits, and validated to reproduce the dataset's shipped
`detection_events.b8` / `obs_flips_actual.b8` **byte-for-byte** for all
420 configurations.

## Columns

| column | type | meaning |
|---|---|---|
| `distance` | int16 | code distance d (3, 5, 7) |
| `basis` | string | `X` or `Z` memory |
| `rounds` | int16 | number of QEC cycles |
| `orientation` | string | patch location label on the chip (e.g. `q4_5`) |
| `shot` | int32 | shot index within the configuration |
| `detectors` | list<bool> | which detectors fired (length `num_detectors`, `= 2 * rounds * (d^2-1)/2`) |
| `observable` | bool | ground-truth logical flip the decoder must predict |

One Parquet shard per `(distance, orientation, basis, rounds)` configuration under
`data/`. The decoder sees only `detectors`; `observable` is the held-out answer key.

## Decoding bundle

Alongside the detection events, each config ships the inputs a decoder panel needs,
keyed by the same `<stem>` (`d{D}_at_{orient}__{basis}__r{rounds:03d}`):

- `circuits/<stem>.stim` — the ideal (noiseless) annotated circuit.
- `dems/<stem>.si1000.dem.gz` — shipped SI1000 detector error model (gzipped).
- `dems/<stem>.rl.dem.gz` — shipped RL-optimized detector error model (gzipped).

With these, the whole evaluation runs off this dataset with no local copy of the
12 GB Willow tree.

## Load

```python
from datasets import load_dataset
ds = load_dataset("ShayManor/willow-surface-code-detection-events", split="train")     # detection events
```