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
Developed and released a comprehensive CUDA-Q Quantum Transformer Tutorial for the NVIDIA/cuda-quantum repository, providing an end-to-end guide for integrating quantum attention into transformer architectures. The work involved constructing quantum circuits, implementing training procedures, and generating molecules, all within a reproducible Python and Jupyter Notebook environment. Detailed documentation and visualization of attention maps were included to illustrate model capabilities and facilitate onboarding for researchers and developers. By delivering a reusable reference implementation, the tutorial accelerated adoption of CUDA Quantum workflows and enabled the scientific computing community to experiment with deep learning and quantum computing techniques in a practical, accessible format.
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
59 peopleSame Organization
@yale.edu24
adamjhnMember
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Brian KoopmanMember
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David BlankenshipMember
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Diego AspinwallMember
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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
Repositories Contributed To
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