Quantum X Labs achieves Meaningful Error Correction Decoder Results with NVIDIA CUDA-Q QEC
Quantum X Labs has completed development steps with the NVIDIA CUDA-Q
Ecosystem
The work
The work is focused on reviewing QXL’s milestone results and draws on NVIDIA
accelerated computing, the NVIDIA CUDA-Q QEC software libraries and, as QXL
progresses from simulation-based validation toward hardware-derived syndrome data
and future real-time decoding.
QXL has completed two meaningful development steps
01
First, the Company executed its Deep Quantum Error Correction (DQEC) workflow on an NVIDIA GPU in an AWS environment and benchmarked its transformer-based QECCT decoder against the classical Minimum-Weight Perfect Matching (MWPM) decoder across controlled toric-code noise configurations. QECCT outperformed MWPM in selected simulated regimes.
02
QXL also tested synthetic surface-code configurations modeled on Google’s public surface-code geometry and experiment structure, spanning multiple code distances. Across these scenarios, the QECCT decoder showed stable logical and bit error rates under varying physical error conditions.
The results
These results are intended as an initial step in validating the approach within controlled simulation environments. Future work will focus on extending these evaluations to publicly available experimental datasets and continuing to refine data pipelines and decoder workflows compatible with CUDA-Q QEC frameworks.
Roadmap
IQCC · Quantum Machines
The broader roadmap also includes QXL’s planned work with IQCC, a Quantum Machines company, to generate hardware-derived syndrome data on superconducting quantum processing hardware.
AI-Based Pre-Decoder
QXL is also reviewing where AI-based pre-decoder workflows using NVIDIA Ising, and low-latency optimization can add the greatest value across these stages of QXL’s roadmap.
"“
These results are meaningful because they move our program from cloud deployment into measured decoder performance and a surface-code data pipeline. We are accelerating QXL’s roadmap with NVIDIA accelerated computing, CUDA-Q QEC and low-latency QEC expertise, while IQCC provides the path to hardware-derived syndrome data. This staged approach is designed to move us from offline benchmarking toward practical real-time QEC."
— Prof. Nir Sharon, Chief Scientist, Quantum X Labs
DQEC technology
QXL’s DQEC technology is based on a proprietary transformer architecture that uses QEC code structure and syndrome information to predict logical corrections. The program is intended to support multiple stabilizer-code workflows and to evaluate the decoder as a full decoder, pre-decoder or hybrid component within accelerated QEC systems.