May 2026 monthly summary for NVIDIA/cudaqx: Focused on delivering quantized ONNX support for AI Decoder Service, robust typing, improved tensor sizing, and end-to-end validation to widen model coverage and improve real-time decoding performance.

Month: 2026-04 — Concise monthly summary of feature work and impact across NVIDIA/cuda-quantum and NVIDIA/cudaqx. Focus on delivering business-value enabled by new runtime configurability and real-time decoding capabilities, plus documentation and demos to accelerate adoption and deployment. No major bug fixes documented this month; emphasis on performance, reliability, and maintainability through new abstractions and clearer tooling.

March 2026 - NVIDIA/cuda-quantum monthly highlights Key features delivered: - Graph Launch Dispatch Refinement: Refined the host-side dispatcher to exclusively handle GRAPH_LAUNCH dispatches, eliminating unused dispatch paths and reducing complexity. This aligns host and device workflows and improves end-to-end reliability for graph execution. Commit: 9ccdc8ba1858dd39a023a311782ffec65db0f1d1 (Add realtime host dispatcher #4180). - Mailbox C API for GRAPH_LAUNCH: Introduced a new C API (cudaq_dispatcher_set_mailbox) to support caller-managed, pinned host mailboxes, ensuring the device-side mailbox pointer can be captured before dispatcher startup. When not provided externally, the API maintains backward compatibility by falling back to internal allocation. - End-to-end tests for GRAPH_LAUNCH: Added comprehensive tests (test_host_dispatcher.cu) including a smoke test and a GRAPH_LAUNCH round-trip to validate correct graph capture, dispatch wiring, and in-place execution results. Major bugs fixed: - Removed unused HOST_CALL and DEVICE_CALL slots from the host dispatcher pipeline, streamlining dispatch flow and reducing surface area for edge-case failures. - Cleaned up dispatch_host_call path and updated relevant headers/comments to reflect the GRAPH_LAUNCH-only design. Overall impact and accomplishments: - Improved reliability and predictability of graph launches with a simplified host dispatcher and robust test coverage. - Stronger alignment between host and device-side mailbox semantics, reducing failure modes related to mailbox handling. - Clear performance and maintainability gains from removing obsolete dispatch paths and centralizing GRAPH_LAUNCH handling. Technologies/skills demonstrated: - C/C++ API design and integration with device-side workflows - End-to-end testing and test harness development for GPU graph execution paths - Systems-level refactoring to reduce complexity and improve throughput Business value: - More predictable graph execution, faster detection of regression through tests, and easier on-ramps for future features that rely on mailbox-driven synchronization.

January 2026 monthly summary for NVIDIA/cudaqx focusing on delivering feature improvements and documentation clarity that enable higher inference performance and easier validation. The work this month emphasizes configurable, high-performance decoding with TensorRT, plus documentation streamlining to reduce future release risk.

December 2025 monthly summary for NVIDIA/cudaqx: Focused on enhancing developer experience by delivering comprehensive TensorRT Decoder API documentation for both Python and C++, including usage examples, parameter references, and hardware compatibility notes. No major bugs fixed this period. Impact includes improved onboarding and faster integration for TensorRT-based workflows, with cross-language doc consistency across Python and C++ APIs.

November 2025: Delivered a GPU-accelerated TensorRT-based Quantum Error Correction (QEC) decoder for the CUDAQX project, along with comprehensive training and deployment documentation to accelerate adoption and reproducibility. Established end-to-end workflows for training data generation, PyTorch model training, ONNX export, and TensorRT inference, enabling scalable, production-ready QEC decoding on GPUs.

Monthly performance summary for 2024-12 focused on NVIDIA/cudaqx workstream. The primary deliverable this month was the Bravyi-Kitaev fermionic transformation implementation in the C++ core with Python bindings and accompanying tests, enabling accurate and scalable quantum chemistry simulations by mapping fermionic operators to spin operators. The work solidifies the repository’s capability for more advanced quantum algorithms and paves the way for streamlined Python-based research workflows.