NVIDIA/cuda-quantum
Aug 2025 – Mar 2026
4 Months active
Languages Used
C++PythonRSTShell
Technical Skills
C++C++ DevelopmentCompiler DevelopmentMLIRPython DevelopmentQuantum Computing
Adam T. Geller
PROFILE
Adam T. Geller
Worked on the NVIDIA/cuda-quantum repository, delivering four features and one bug fix over four months focused on quantum computing and compiler optimization. Developed a simulator-based resource estimation system, replacing approximate methods with precise tracking of qubit and gate usage, and enabled remote resource estimation through kernel execution refactoring. Implemented phase folding optimizations to consolidate Rz gates, reducing circuit complexity and improving efficiency. Enhanced Python kernel workflows for GKE by addressing host entry point issues and improved phase folding aliasing for more accurate resource estimation. Utilized C++, Python, and MLIR, demonstrating depth in algorithm design, kernel development, and testing practices.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
4
Lines of code
4,814
Activity Months4
Work History
March 2026
2 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — NVIDIA/cuda-quantum delivered two feature-level improvements that drive reliability and performance for Python kernel workloads on GKE and for quantum optimization. Key outcomes include: (1) ignoring dummy host entry points for kernels built with the Python kernel builder in GKE to prevent host-function-related errors and improve execution smoothness; (2) phase folding aliasing optimization improvements for more robust handling, leading to more accurate quantum resource estimation and better optimization performance. These efforts illustrate strong kernel-building workflows, GKE integration, and phase-folding optimization techniques, delivering business value through fewer run-time failures and faster, more reliable quantum workloads.
2 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — NVIDIA/cuda-quantum delivered two feature-level improvements that drive reliability and performance for Python kernel workloads on GKE and for quantum optimization. Key outcomes include: (1) ignoring dummy host entry points for kernels built with the Python kernel builder in GKE to prevent host-function-related errors and improve execution smoothness; (2) phase folding aliasing optimization improvements for more robust handling, leading to more accurate quantum resource estimation and better optimization performance. These efforts illustrate strong kernel-building workflows, GKE integration, and phase-folding optimization techniques, delivering business value through fewer run-time failures and faster, more reliable quantum workloads.
March 2026
February 2026
1 Commits
Feb 1, 2026Month 2026-02: Focused on stabilizing CUDA-Q QIR validation on emulated targets and ensuring correct LLVM code generation. Delivered targeted bug fixes and small-code improvements that reduce validation noise and improve emulation reliability, enabling faster iteration and more predictable results for downstream users.
February 2026
1 Commits
Feb 1, 2026Month 2026-02: Focused on stabilizing CUDA-Q QIR validation on emulated targets and ensuring correct LLVM code generation. Delivered targeted bug fixes and small-code improvements that reduce validation noise and improve emulation reliability, enabling faster iteration and more predictable results for downstream users.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — NVIDIA/cuda-quantum: Phase Folding Optimization for Rz Gate Consolidation delivered; introduced an environment-flag controlled optimization reducing gate count, with new tests and validation utilities. No major bugs fixed this period; overall impact includes improved circuit efficiency and test coverage; demonstrates CUDA-quantum stack proficiency, testing discipline, and environment-driven features.
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — NVIDIA/cuda-quantum: Phase Folding Optimization for Rz Gate Consolidation delivered; introduced an environment-flag controlled optimization reducing gate count, with new tests and validation utilities. No major bugs fixed this period; overall impact includes improved circuit efficiency and test coverage; demonstrates CUDA-quantum stack proficiency, testing discipline, and environment-driven features.
October 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for NVIDIA/cuda-quantum focused on enhancing resource estimation capabilities and enabling remote experimentation. Delivered a simulator-based resource counting system that replaces the previous estimate_resources approach, added a conditional function to handle measurement-based branching, and refactored kernel execution to support remote simulators with accompanying tests to validate correctness across local and remote environments. These changes significantly improve accuracy, scalability, and reproducibility for quantum workload planning.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for NVIDIA/cuda-quantum focused on enhancing resource estimation capabilities and enabling remote experimentation. Delivered a simulator-based resource counting system that replaces the previous estimate_resources approach, added a conditional function to handle measurement-based branching, and refactored kernel execution to support remote simulators with accompanying tests to validate correctness across local and remote environments. These changes significantly improve accuracy, scalability, and reproducibility for quantum workload planning.
Activity
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Quality Metrics
Correctness83.4%
Maintainability81.6%
Architecture83.4%
Performance80.0%
AI Usage26.6%
Skills & Technologies
Programming Languages
C++PythonRSTShell
Technical Skills
Algorithm DesignC++C++ DevelopmentC++ developmentCUDACompiler DesignCompiler DevelopmentCompiler OptimizationKernel developmentMLIROptimizationPython DevelopmentPython developmentQuantum ComputingRemote Systems
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