genQC logo genQC
  • Overview
  • Get Started
  • Tutorials
  • API Reference
  • Research
  • Code Repository
  1. Models
  2. Transformers
  3. Transformers and attention

API Reference

  • Modules Overview
  • Release notes

  • Benchmark
    • Compilation benchmark
  • Dataset
    • Dataset balancing
    • Cached dataset
    • Quantum circuit dataset
    • Config dataset
    • Dataset helper functions
    • Mixed cached dataset
  • Inference
    • Evaluation metrics
    • Evaluation helper
    • Sampling functions
  • Models
    • Config model
    • Frozen OpenCLIP
    • Layers
    • Position encodings
    • Conditional qc-UNet
    • Encoder for unitaries
    • Clip
      • Frozen OpenCLIP
      • Unitary CLIP
    • Embedding
      • Base embedder
      • Rotational preset embedder
    • Transformers
      • Transformers and attention
      • CirDiT - Circuit Diffusion Transformer
      • Transformers
  • Pipeline
    • Callbacks
    • Compilation Diffusion Pipeline
    • Diffusion Pipeline
    • Diffusion Pipeline Special
    • Metrics
    • Multimodal Diffusion Pipeline
    • Pipeline
    • Unitary CLIP Pipeline
  • Platform
    • Circuits dataset generation functions
    • Circuits instructions
    • Simulation backend
    • Backends
      • Base backend
      • CUDA-Q circuits backend
      • Pennylane circuits backend
      • Qiskit circuits backend
    • Tokenizer
      • Base tokenizer
      • Circuits tokenizer
      • Tensor tokenizer
  • Scheduler
    • Scheduler
    • DDIM Scheduler
    • DDPM Scheduler
    • DPM Scheduler
  • Utils
    • Async functions
    • Config loader
    • Math and algorithms
    • Miscellaneous util

On this page

  • Feed-forward
    • FeedForwardBlock
  • Attention blocks
    • BasisSelfAttnBlock
    • BasisCrossAttnBlock
  • Spatial residual transformers
    • SpatialTransformerSelfAttn
    • SpatialTransformer
  • Report an issue
  • View source
  1. Models
  2. Transformers
  3. Transformers and attention

Transformers and attention

Common transformer and attention blocks.

Feed-forward


source

FeedForwardBlock

 FeedForwardBlock (in_dim:int, hidden_dim:int, dropout:float=0.0)

A small dense feed-forward network as used in transformers. Assumes channel last. Inspired by https://arxiv.org/pdf/2401.11605. From https://arxiv.org/pdf/2002.05202 a modification to SiGLU

Attention blocks


source

BasisSelfAttnBlock

 BasisSelfAttnBlock (ch, num_heads, dropout=0.0, batch_first=False)

A self attention block, i.e. a transformer encoder.


source

BasisCrossAttnBlock

 BasisCrossAttnBlock (ch, num_heads, dropout=0.0, batch_first=False)

A cross attention block, i.e. a transformer decoder.

Spatial residual transformers


source

SpatialTransformerSelfAttn

 SpatialTransformerSelfAttn (ch, num_heads, depth, dropout=0.0,
                             num_groups=32)

A spatial residual transformer, only uses self-attention.


source

SpatialTransformer

 SpatialTransformer (ch, cond_emb_size, num_heads, depth, dropout=0.0,
                     num_groups=32)

A spatial residual transformer, uses self- and cross-attention on conditional input.

Back to top
Rotational preset embedder
CirDiT - Circuit Diffusion Transformer
 

Copyright 2025, Florian Fürrutter

  • Report an issue
  • View source