Ising-Decoder-ColorCode-1-Fast

Ising-Decoder-ColorCode-1-Fast Overview Description: Ising-Decoder-ColorCode-1-Fast predicts physical space-time corrections that sparsify the input syndrome (producing a residual syndrome) for triangular color code quantum error correction circuits, reducing logical error rates by at least 2x when combined with a standard global color code decoder. Ising-Decoder-ColorCode-1-Fast v0.1.0 was developed by NVIDIA. This model is ready for commercial or non-commercial use. Publication This implementation accompanies the paper: Jan Olle, Christopher Chamberland, Muyuan Li, Igor Baratta, "Fast and accurate AI-based pre-decoders for color codes" arXiv:2607.10058, 2026. doi:10.48550/arXiv.2607.10058 Please cite the paper if you use this repository in research or published work. License/Terms of Use: GOVERNING TERMS: Use of this model is governed by the OpenMDW-1.1 License. Deployment Geography: Global Use Case: Quantum computing researchers and engineers building triangular color code QEC systems who need a lightweight predecoder to reduce logical error rates and accelerate decoding throughput alongside any standard global color code decoder (we use Chromobius as an open-source example). Release Date: Hugging Face 07/21/2026 via https://huggingface.co/nvidia/Ising-Decoder-ColorCode-1-Fast Reference(s): NVIDIA Quantum Predecoder Chromobius color code decoder Model Architecture: Architecture Type: Convolutional Neural Network (CNN) Network Architecture: Custom 3D CNN — bottleneck residual cascade of same-padded 3D convolutions (predecoder_memory_cascade) Number of model parameters: ~2.94x10^6 (2,936,580) The pre-decoder is a lightweight 3D fully-convolutional neural network (CNN) that processes color code syndrome volumes across space and time. Layers The network is a bottleneck residual cascade of same-padded 3D convolutions (padding = kernel_size // 2), so the spatial and temporal dimensions are preserved through every layer. A plain 3D-convolution stem lifts the 4 input cha