Datasets:

SQuADDS
/

SQuADDS_Layout_Embeddings

license: mit
configs:
  - config_name: static-embedding-v0
    data_files:
      - split: train
        path: metadata/static-embedding-v0.parquet
tags:
  - quantum
  - superconducting
  - qiskit-metal
  - gds
  - layout
  - embedding
  - geometry
  - computer-vision
pretty_name: SQuADDS Layout Embeddings
size_categories:
  - 1K<n<10K

SQuADDS Layout Embeddings

Versioned layout representations for the 4,577 GDS artifacts in SQuADDS/SQuADDS_Layouts.

Static embedding model `v0`

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

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 is intended as a transparent baseline and as a stable input for similarity search. A future v1 may use a compact learned raster or geometry-graph encoder, but will be published as a separate model rather than changing v0 in place.

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

Coverage and links

Component	Embeddings
`GeneralizedCapNInterdigital`	3,683
`CapNInterdigitalTee`	894

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.

Access

from squadds.layouts import StaticEmbeddingClient

client = StaticEmbeddingClient()
record = client.get("layout:sha256:<layout hash>")
bitmap = client.shape_bitmap(record["layout_id"])
neighbors = client.nearest(record["layout_id"], limit=10)

SQuADDS_DB rows can resolve the same vector directly with SQuADDS_DB.get_layout_embedding(row). 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. Simulation results and design options live in 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:

@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.