shiavm006 opened a new issue, #749: URL: https://github.com/apache/mahout/issues/749
### Summary Add built-in parameter optimization utilities to QuMat, including the parameter shift rule for gradient computation and optimizer wrappers for training parameterized quantum circuits (PQCs). This will eliminate the need for users to write optimization boilerplate code and make QuMat a more complete quantum ML framework. ### Use Case Users currently write 50+ lines of custom optimization loops. The `docs/qumat_gap_analysis_for_pqc.md` explicitly identifies this as missing: - Section 4: "No functionality for optimizing parameters" - Part 2, Section 1: "Parameter Shift Rule Implementation" listed as "Required Additions" - Part 2, Section 4: "Optimization Module" listed as "Required Additions" Current: Users write manual scipy.optimize loops with parameter binding Proposed: `optimize_parameters(qumat, cost_fn, initial_params, method="gradient_descent")` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
