
Worked on the NVIDIA/cuda-quantum repository to deliver dynamic hardware support for IQM quantum processing units, focusing on runtime architecture fetching and improved handling of qubit calibration and mapping. Leveraged C++ and Python to integrate APIs that enable the system to adapt to hardware changes efficiently. Addressed test suite performance by deactivating a time-consuming test on the IQM backend, which reduced continuous integration costs and accelerated feedback cycles. The work demonstrated a strong understanding of quantum computing concepts and testing strategies, resulting in more reliable hardware integration and streamlined development workflows within the CUDA Quantum project during the reported period.
November 2025 monthly summary for NVIDIA/cuda-quantum focused on delivering robust dynamic hardware support for IQM QPUs and improving test suite efficiency. The work emphasized business value through reliable hardware integration, faster feedback loops, and reduced CI costs.
November 2025 monthly summary for NVIDIA/cuda-quantum focused on delivering robust dynamic hardware support for IQM QPUs and improving test suite efficiency. The work emphasized business value through reliable hardware integration, faster feedback loops, and reduced CI costs.

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