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To Do List for SQuADDS:

Refer to contribution guidelines for more information on how to contribute code.

Bug Fixes:

  • Addressing issues on GitHub
  • Addressing TODOs in the code
  • Fixing any bugs in the code
  • Robustly handling caching and environment variables setup for all OS (some Windows users had issues)
  • Check front-end UI code for thoroughly against API calls

Simulations:

  • Stress-test and report any bugs for Ansys simulations using the SQuADDS package
  • Add guesses for bare resonator frequencies (50 ohm impedance matched) using API calls to https://www.microwaves101.com/calculators/864-coplanar-waveguide-calculator
  • Add more resonator property calculator methods via https://github.com/ooovector/cpw_coupling/blob/master/Conformal%20mapping%20of%20a%20CPW%20coupler.ipynb integration - may be a GUI? (https://smm.misis.ru/CPW-resonator-coupling/)
  • Generalize SQDMetal Palace simulations to work with any HPC and make a PR
  • Conduct hyperparameter optimization study of Palace simulations and test for reliability/repeatability
  • Integrate SQDMetal (fixed version) as a dependency of SQuADDS
  • Add an easy Palace simulation API on SQuADDS (with parallel processing)
  • Write a comprehensive tutorial on how to use Palace for simulations
  • Make Ansys simulation handle arbitrary geometries for eigenmode and cap matrix simulations
  • Provide compute resources to run simulations covering sparse regions of the Hamiltonian space
  • Add MCP tooling for driven-modal HFSS simulations (expose run_drivenmodal, capacitance/coupled-system port builders, checkpoint/artifact discovery, and admittance/Hamiltonian post-processing as MCP tools so agents can drive Tutorials 10–13 end-to-end)

Core:

  • Create a simple API for users to contribute experimental data to SQuADDS_DB
  • Handle cases where the user does not wish to specify a resonator_type
  • Improve system design for both the SQuADDS package and SQuADDS_DB
  • Develop a system to "metalize" any .gds/.dxf file (i.e., generate the corresponding Qiskit Metal file from the CAD file)
  • Add support to handle designs generated via other tools (explicitly not Qiskit Metal), such as:
  • Provide APIs (modules/methods) to easily add more data columns to existing simulation entries in the database (e.g., allowing users to rerun geometries and add participation ratios)
  • Transition datasets to SQLite or another format to handle larger-than-memory datasets as we scale
  • Refactor code to implement faster methods with lower memory usage for handling DataFrame operations
  • Speeding up the process for front-end UI user flow

Contribution:

  • Use local LLMs/free secure LLMs to create/update the measured_device dataset using the GitHub repo
  • Utilize HuggingFace Hub API for handling contributions to the database in a more streamlined and automated way (e.g., create clone, branch, PR, etc.)
  • Implement and deploy an acceptance server for handling contributions to the database (calculates simulation and measured value discrepancies, automatically simulates representative data points for reliability, notifies maintainers for approval) [Not needed in the immediate future]
  • Automate integration of CAD files along with their measured Hamiltonian parameters [Not needed in the immediate future]

Machine Learning (ML):

  • Develop an architecture/framework for deploying ML models on SQuADDS (via HuggingFace endpoints/spaces, in code, etc.)
  • Add MCP tooling for hosted SQuADDS ML models (wrap the SQuADDS ML Inference API as MCP tools so agents can discover available models via GET /models and run inverse-design predictions via POST /predict without hand-writing HTTP calls)
  • Provide APIs (modules/methods) for incorporating ML interpolation features into SQuADDS
  • Utilize HuggingFace Tasks for ML applications
  • Identify relevant design space variables for any system given a set of $\hat{H}$ parameters using encoders
  • Determine analytical dependence of $\hat{H}$ parameters on design space variables using Kolmogorov-Arnold Networks (KANs)
  • Expand training datasets using cGANs/VAEs/PINNs (i.e., attempt to simulate the simulator)
  • Build upon the work by Elie Genois et al., 2021
  • system to add measured datasets to the database straight from arxiv/target journal papers

Workflows:

  • Add any other workflow that assists developers in contributing

Feature Requests:

  • DRY sims
  • async ansys
  • add chi to the complete df
  • Enable users to add methods with computation and append to merged_df for search in the Analyzer module
  • Allow users to pass a circuit from SQCircuits and SQuADDS to provide a first-guess physical layout in Qiskit Metal
  • Incorporate SCILLA and/or its applications
  • Add interpolation of individual components (both API and UI)

Boring but Necessary:

  • say that coupling is capacitive and handle inductive logic
  • order of select_system and specifying it as we grow beyond two
  • Check for breaking changes in the latest version of dependencies and update the package accordingly
  • add input type error handling to sim code
  • automated kink detection + meander smoothing prior to qiskit-metal rendering
  • letting users choose the .env file location OR telling them where to find it
  • Check for claw dimensions on the Hamiltonian space plot
  • Create unit tests for each feature/file
  • Establish proper train/test splits and modify SQuADDS_DB() to always return all data
  • Standardize the handling of units for simulated results and implement necessary backend changes
  • Add more tutorials on how to use the package and its various applications
  • Verify if the precision of design parameters is handled correctly and fix as needed
  • Change all instances of NCap to CapNInterdigital
  • Separate the documentation from the main package

Fancy/For Fun:

  • Implement LLM-based queries for SQuADDS using pandas-ai (support for OpenAI and local LLaMA models)
  • Add visual components to the SQuADDS UI