Datasets:

SQuADDS
/

SQuADDS_Layout_Embeddings

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

SQuADDS Layout Embeddings

Versioned layout representations for the 24,106 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 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

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. 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.