NVIDIA/cuda-quantum
Mar 2025 – Mar 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
CUDA QuantumData ScienceDeep LearningMachine LearningPythonQuantum Computing
Anthony M. Smaldone
PROFILE
Anthony M. Smaldone
Anthony Smaldone developed a comprehensive CUDA-Q Quantum Transformer Tutorial for the NVIDIA/cuda-quantum repository, focusing on integrating quantum attention mechanisms within transformer architectures. He designed the tutorial to guide users through setup, quantum circuit construction, training procedures, and molecule generation, culminating in the visualization of attention maps to illustrate model behavior. Using Python and Jupyter Notebook, Anthony provided detailed documentation and reproducible code, enabling researchers to experiment with and benchmark quantum transformer models. His work addressed the need for accessible onboarding resources in quantum machine learning, offering a reusable reference that deepened understanding of CUDA Quantum workflows and scientific computing techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
5,158
Activity Months1
Your Network
50 people
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025In March 2025, delivered the CUDA-Q Quantum Transformer Tutorial rollout for NVIDIA/cuda-quantum. This end-to-end tutorial demonstrates quantum attention integrated into a transformer architecture, covering setup, circuit construction, training procedures, molecule generation, and visualization of attention maps to showcase model capabilities. The release provides a reproducible reference for researchers and accelerates onboarding into CUDA-Q workflows, strengthening the project’s educational impact and adoption.
1 Commits • 1 Features
Mar 1, 2025In March 2025, delivered the CUDA-Q Quantum Transformer Tutorial rollout for NVIDIA/cuda-quantum. This end-to-end tutorial demonstrates quantum attention integrated into a transformer architecture, covering setup, circuit construction, training procedures, molecule generation, and visualization of attention maps to showcase model capabilities. The release provides a reproducible reference for researchers and accelerates onboarding into CUDA-Q workflows, strengthening the project’s educational impact and adoption.
March 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
CUDA QuantumData ScienceDeep LearningMachine LearningPythonQuantum ComputingScientific ComputingTransformer Networks
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