Community Sync 2026-02-28

[ryankert01]

The participants mentioned in the transcript include:

• Vic
• Shivam
• Ryan Huang

Here is a summary of the two proposals and the discussion points:

Proposal 1: Enhancing Documentation and Developer Experience (by Vic)

The primary goal is to address outdated API documents and improve consistency to enhance developer and user experience, especially when using AI tools for development.

Key Features and Discussion:

• Pre-commit Hook: Vic plans to add a pre-commit check to ensure documentation is always in sync and not broken with every commit, including checking for errors in docstrings.
• Docstring Consistency: Docstrings in the code must follow certain rules. Ryan Huang suggested creating an "agent skill" for this, similar to what is used in Apache Airflow, to help newcomers and ensure consistency.
• Automated Versioning (Plus Feature): Vic proposed using Git tags to auto-update version numbers (e.g., 0.5 to 0.6) for stable releases, triggering a CI update. Ryan Huang suggested this functionality might be better as a standalone script.sh file rather than integrating it into the CI pipeline on "week seven."
• Timeline: Includes modifying existing docstrings, integrating them into the website and CI, and finally improving guides and documentation (e.g., adding "action markdown").
• Consistency Concern: Shivam raised a concern about how a consistent formatting structure would be maintained across all documents if the process is automated. Vic clarified that the pre-commit checks would handle this consistency and help developers find issues early in their local environment.

Proposal 2: Implementing a Z Feature Map (by Shivam)

This proposal focuses on the implementation of a feature map, likely for the QDP (Quantum Data Processing) project, prioritizing numerical correctness and speed.

QDP Decision Principles:

Ryan Huang stated the two most important factors for decision-making in QDP are:

1. Correctness (First Priority): Code must be numerically stable with high precision, adhering to set tolerance rules.
2. Speed (Second Priority): The chosen method must be the fastest available.

Implementation Details and Discussion:

• Feature Map Approach: Shivam prefers Option 1, which uses already prepared angles, prioritizing speed. He acknowledged that the numerical correctness of this part needs improvement.
• API Configuration: Shivam is leaning toward defining variables with simpler names to fix the current design and avoid risky API changes, and ensuring the API is consistent with major QML frameworks like "quizkit."
• Work Plan: Includes finalizing the parameter layout, implementing input validations, uploading parameters for memory, and defining CUDA launch entry points. Integration will be via existing QDP APIs without changing the current design.
• QDA Kernel: Implementing the kernel computing for the Z map feature state vectors, with testing planned using an Nvidia 3090 GPU.
• Tooling/Environment: Shivam is currently using vas.ai but is planning to switch to runpod based on senior advice that it offers more proficient GPUs.
• Background: Shivam recently started learning CUDA as he was previously contributing to the "cumat part" and primarily worked on Mac OS.
• Feedback from Ryan Huang: Ryan Huang stressed the importance of adding a benchmark part to the proposal. This benchmark should compare all available options for the encoding method in different situations to find the fastest one and document when to use each method.