NVIDIA/cudaqx
Jan 2025 – Apr 2025
3 Months active
Languages Used
C++CMakePythonrstYAML
Technical Skills
C++ DevelopmentCMakeQuantum Error CorrectionAPI DesignDocumentationPython Development
Justin Gage Lietz
PROFILE
Justin Gage Lietz
During a three-month period, John Lietz extended the CUDA-Q QEC capabilities in the NVIDIA/cudaqx repository by implementing surface code support for quantum error correction. He designed core C++ data structures and CMake-based helpers to enable robust surface-code workflows on CUDA, laying the foundation for scalable GPU-accelerated experiments. John also addressed API consistency by refining the sample_memory_circuit output, ensuring reliable syndrome data for downstream analysis in Python. Additionally, he improved the fidelity of circuit-level noise simulations by correcting per-shot syndrome processing, leveraging Python scripting and CI/CD configuration to validate changes. His work demonstrated depth in quantum error correction engineering.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
1,192
Activity Months3
Your Network
1359 people
Work History
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for NVIDIA/cudaqx focused on quality and correctness improvements in the circuit-level noise simulation. The key deliverable was a bug fix correcting per-shot syndrome processing by reshaping syndromes into per-shot arrays, ensuring accurate per-shot behavior in the noise simulation example. The change was implemented in the commit 2f3b6799650887067c4c89aa5f55ea8f21f1639a with the message 'Correcting syndromes per shot in circuit-level example (#136)'. Validation included updated checks to guard against regressions in per-shot processing. This work enhances simulation fidelity, reliability, and user trust in per-shot noise modeling, and lays groundwork for more robust per-shot evaluation across the circuit-level noise workflow.
1 Commits
Apr 1, 2025April 2025 monthly summary for NVIDIA/cudaqx focused on quality and correctness improvements in the circuit-level noise simulation. The key deliverable was a bug fix correcting per-shot syndrome processing by reshaping syndromes into per-shot arrays, ensuring accurate per-shot behavior in the noise simulation example. The change was implemented in the commit 2f3b6799650887067c4c89aa5f55ea8f21f1639a with the message 'Correcting syndromes per shot in circuit-level example (#136)'. Validation included updated checks to guard against regressions in per-shot processing. This work enhances simulation fidelity, reliability, and user trust in per-shot noise modeling, and lays groundwork for more robust per-shot evaluation across the circuit-level noise workflow.
April 2025
February 2025
1 Commits
Feb 1, 2025February 2025: Delivered a targeted bug fix in NVIDIA/cudaqx to ensure consistent sample_memory_circuit output and improved data reliability for QEC experiment analysis. The API now returns nRounds of syndrome data consistently, instead of nRounds-1 XOR'd syndromes plus the initial shot, enabling uniform downstream processing and simpler analytics.
February 2025
1 Commits
Feb 1, 2025February 2025: Delivered a targeted bug fix in NVIDIA/cudaqx to ensure consistent sample_memory_circuit output and improved data reliability for QEC experiment analysis. The API now returns nRounds of syndrome data consistently, instead of nRounds-1 XOR'd syndromes plus the initial shot, enabling uniform downstream processing and simpler analytics.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) – Focused on extending CUDA-Q QEC capabilities with Surface Code support in NVIDIA/cudaqx. Implemented core data structures and layout helpers to enable robust surface-code QEC workflows on CUDA, establishing a foundation for scalable GPU-accelerated quantum error correction experiments.
1 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01) – Focused on extending CUDA-Q QEC capabilities with Surface Code support in NVIDIA/cudaqx. Implemented core data structures and layout helpers to enable robust surface-code QEC workflows on CUDA, establishing a foundation for scalable GPU-accelerated quantum error correction experiments.
January 2025
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture86.6%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakePythonYAMLrst
Technical Skills
API DesignC++ DevelopmentCI/CD ConfigurationCMakeDocumentationPython DevelopmentPython ScriptingQuantum Error Correction
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