NVIDIA/cudaqx
Dec 2024 – Jan 2026
4 Months active
Languages Used
C++PythonYAMLRSTbash
Technical Skills
Algorithm ImplementationC++ DevelopmentPython DevelopmentQuantum ChemistryQuantum ComputingUnit Testing
Scott Thornton
PROFILE
Scott Thornton
Over four months, Wsttiger developed advanced quantum computing features for the NVIDIA/cudaqx repository, focusing on scalable quantum chemistry simulations and high-performance quantum error correction. They implemented the Bravyi-Kitaev fermionic transformation in C++ with Python bindings, enabling accurate operator mapping for quantum algorithms. Wsttiger also delivered a GPU-accelerated TensorRT-based decoder, integrating PyTorch model training, ONNX export, and CUDA optimization for efficient inference. Their work included YAML-based configuration, batch decoding improvements, and comprehensive API documentation in both Python and C++. The engineering demonstrated depth in algorithm implementation, performance optimization, and cross-language API design, supporting robust, production-ready quantum computing workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
5
Lines of code
5,926
Activity Months4
Your Network
16 people
Work History
January 2026
5 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for NVIDIA/cudaqx focusing on delivering feature improvements and documentation clarity that enable higher inference performance and easier validation. The work this month emphasizes configurable, high-performance decoding with TensorRT, plus documentation streamlining to reduce future release risk.
5 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for NVIDIA/cudaqx focusing on delivering feature improvements and documentation clarity that enable higher inference performance and easier validation. The work this month emphasizes configurable, high-performance decoding with TensorRT, plus documentation streamlining to reduce future release risk.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for NVIDIA/cudaqx: Focused on enhancing developer experience by delivering comprehensive TensorRT Decoder API documentation for both Python and C++, including usage examples, parameter references, and hardware compatibility notes. No major bugs fixed this period. Impact includes improved onboarding and faster integration for TensorRT-based workflows, with cross-language doc consistency across Python and C++ APIs.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for NVIDIA/cudaqx: Focused on enhancing developer experience by delivering comprehensive TensorRT Decoder API documentation for both Python and C++, including usage examples, parameter references, and hardware compatibility notes. No major bugs fixed this period. Impact includes improved onboarding and faster integration for TensorRT-based workflows, with cross-language doc consistency across Python and C++ APIs.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a GPU-accelerated TensorRT-based Quantum Error Correction (QEC) decoder for the CUDAQX project, along with comprehensive training and deployment documentation to accelerate adoption and reproducibility. Established end-to-end workflows for training data generation, PyTorch model training, ONNX export, and TensorRT inference, enabling scalable, production-ready QEC decoding on GPUs.
2 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a GPU-accelerated TensorRT-based Quantum Error Correction (QEC) decoder for the CUDAQX project, along with comprehensive training and deployment documentation to accelerate adoption and reproducibility. Established end-to-end workflows for training data generation, PyTorch model training, ONNX export, and TensorRT inference, enabling scalable, production-ready QEC decoding on GPUs.
November 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024Monthly performance summary for 2024-12 focused on NVIDIA/cudaqx workstream. The primary deliverable this month was the Bravyi-Kitaev fermionic transformation implementation in the C++ core with Python bindings and accompanying tests, enabling accurate and scalable quantum chemistry simulations by mapping fermionic operators to spin operators. The work solidifies the repository’s capability for more advanced quantum algorithms and paves the way for streamlined Python-based research workflows.
December 2024
1 Commits • 1 Features
Dec 1, 2024Monthly performance summary for 2024-12 focused on NVIDIA/cudaqx workstream. The primary deliverable this month was the Bravyi-Kitaev fermionic transformation implementation in the C++ core with Python bindings and accompanying tests, enabling accurate and scalable quantum chemistry simulations by mapping fermionic operators to spin operators. The work solidifies the repository’s capability for more advanced quantum algorithms and paves the way for streamlined Python-based research workflows.
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture100.0%
Performance95.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++PythonRSTYAMLbash
Technical Skills
API designAPI developmentAlgorithm ImplementationC++ DevelopmentC++ developmentCUDACUDA programmingMachine LearningPerformance OptimizationPyTorchPython DevelopmentPython developmentQuantum ChemistryQuantum ComputingTensorRT
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