README.md

---
license: mit
configs:
- config_name: static-embedding-v0
  data_files:
  - split: train
    path: "metadata/static-embedding-v0.parquet"
- config_name: universal-geometry-v1
  data_files:
  - split: train
    path: "metadata/universal-geometry-v1.parquet"
tags:
- quantum
- superconducting
- qiskit-metal
- gds
- layout
- embedding
- geometry
- computer-vision
pretty_name: SQuADDS Layout Embeddings
size_categories:
- 10K<n<100K
---

<center>
  <img src="https://github.com/LFL-Lab/SQuADDS/blob/master/docs/_static/images/squadds_logo_dark_name.png?raw=true" width="80%" alt="SQuADDS Logo" />
</center>

# SQuADDS Layout Embeddings

Versioned layout representations for the 24,106 GDS artifacts in
[SQuADDS/SQuADDS_Layouts](https://huggingface.co/datasets/SQuADDS/SQuADDS_Layouts).

## Static embedding model `v0`

`static-embedding-v0` implements the original SQuADDS proof-of-concept model:

```text
v0 = parameter_sum + geometric_moments + flattened_shape_bitmap
```

Each unit-normalized vector has **9,227 dimensions**:

| Block | Dimensions | Contents |
| --- | ---: | --- |
| Parameter sum | 1 | Permutation- and parameter-count-invariant sum of numerical design options, converted to micrometers where units are present |
| Geometric moments | 10 | Functional area, perimeter, aspect ratio, occupancy, centroid, second central moments, and eccentricity |
| Shape tensor | 9,216 | Row-major flattened 96×96 signed bitmap of functional GDS geometry |

The bitmap uses `+1` for conductor, `-1` for etch, `+0.5` for explicit port
geometry, and `0` for background. Geometry is cropped to its functional bounds,
centered without distortion, supersampled at 4×, and reduced to 96×96. The
large simulation-domain ground rectangle on layer `(1, 0)` is excluded so it
does not hide the component shape.

This is a deterministic static embedding, not a learned model. It remains a
transparent baseline and a stable input for similarity search.

The exact block offsets, moment order, raster semantics, normalization
statistics, and source schema are frozen in
`metadata/static-embedding-v0.schema.json`.

## Universal geometry model `v1`

`universal-geometry-v1` is an additive 1,024-dimensional standard built from only
a GDS file, a functional layer-role mapping, and the native design-parameter
dictionary. Simulation targets are never embedded.

| Block | Dimensions | Contents |
| --- | ---: | --- |
| Geometry metrics | 32 | Centered physical and morphological metrics; availability remains metadata rather than distorting cosine distance |
| Multiscale shape | 768 | Target-blind, variance-selected full-spectrum 2D DCT coefficients from 96×96 signed-material and boundary-distance rasters |
| Parameter controls | 224 | Stable signed feature hashing of canonical parameter paths after per-parameter centering and scaling |

The metric block retains physical scale and role-specific conductor, etch, and
port measurements. The shape block captures finger topology and boundary detail
without storing a dense pixel tensor. Each block is normalized and explicitly
weighted, and every fitted statistic and selected spectral frequency is frozen
in the schema.

Parameter identity is retained on every row through `parameter_names`,
`parameter_values`, `parameter_hash_indices`, and `parameter_hash_signs`.
`models/universal-geometry-v1/control-map.parquet` provides the global,
auditable bridge back to the originating layout controls.

This first v1 configuration contains all **20,062**
`GeneralizedCapNInterdigital` designs. The encoder accepts foreign GDS layouts
when their `(layer, datatype)` pairs are mapped to `conductor`, `etch`, or
`port`; the cross-component reference normalization will be frozen in a later
release after it is calibrated on the full SQuADDS catalogue.

The complete input contract, block offsets, transforms, normalization
statistics, and invariances are frozen in
`models/universal-geometry-v1/schema.json`.

The earlier 512-dimensional v1.0 candidate was rejected before release because
its 8×8 low-pass shape crop lost finger detail and its common offsets collapsed
cosine similarities. V1.1 passed paired topology, parameter-locality, shape,
held-out capacitance-locality, and similarity-dynamic-range gates against v0
across five deterministic held-out samples. Capacitance was never used to fit
the embedding.

## Coverage and links

| Component | Embeddings |
| --- | ---: |
| `GeneralizedCapNInterdigital` | 20,062 |
| `CapNInterdigitalTee` | 894 |
| `CavityClawRouteMeander` | 1,216 |
| `TransmonCross` | 1,934 |

Every row retains `layout_id`, `artifact_id`, `design_id`, `component_name`,
and `source_id`, plus the raw parameter sum, geometric moments, functional
bounds, and a SHA-256 hash of the 96×96 bitmap.

The normalization statistics in this release are fit across all four component
families. Existing layout identities and shape bitmaps remain stable; vectors
are republished together so cosine similarity remains comparable across the
complete catalogue.

## Access

```python
from squadds.layouts import LayoutEmbeddingClient, StaticEmbeddingClient

v0 = StaticEmbeddingClient()  # Backward-compatible alias
v1 = LayoutEmbeddingClient(version="v1")

record = v1.get("layout:sha256:<layout hash>")
neighbors = v1.nearest(record["layout_id"], limit=10)
schema = v1.schema()
controls = v1.control_map()
```

SQuADDS_DB rows can resolve the same vector directly with
`SQuADDS_DB.get_layout_embedding(row, embedding_version="v1")`. Omitting the
version preserves the v0 default. The SQuADDS MCP server also provides
`get_layout_embedding` and `find_similar_layouts`.

## Provenance

Raw GDS artifacts, layer semantics, checksums, and geometry features live in
[SQuADDS/SQuADDS_Layouts](https://huggingface.co/datasets/SQuADDS/SQuADDS_Layouts).
Simulation results and design options live in
[SQuADDS/SQuADDS_DB](https://huggingface.co/datasets/SQuADDS/SQuADDS_DB).
The generalized-capacitor dataset was contributed by Saikat Das of the
Levenson-Falk Lab at USC.

## Citation

If you use this dataset, cite SQuADDS:

```bibtex
@article{Shanto2024squaddsvalidated,
  doi = {10.22331/q-2024-09-09-1465},
  title = {{SQ}u{ADDS}: {A} validated design database and simulation workflow for superconducting qubit design},
  author = {Shanto, Sadman and Kuo, Andre and Miyamoto, Clark and Zhang, Haimeng and Maurya, Vivek and Vlachos, Evangelos and Hecht, Malida and Shum, Chung Wa and Levenson-Falk, Eli},
  journal = {{Quantum}},
  volume = {8},
  pages = {1465},
  year = {2024}
}
```

This dataset is licensed under the [MIT License](https://github.com/LFL-Lab/SQuADDS/blob/master/LICENSE).