tensorflow / tensorflow/quantum
Remove use of finite different in Adjoint gradient
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Description
Currently the Adjoint gradient method in C++ uses finite difference of gates to calculate the "gradient gate" to apply to the state. In terms of numerical precision this isn't ideal. What would be better is to slowly swap out the "gradient gate" calculation using finite difference to instead use analytic/closed forms. @zaqqwerty
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the C++ implementation of the Adjoint gradient method and the finite-difference calculation of the gradient gate. Review which gates are covered and define completion as replacing that calculation with analytic or closed-form forms, while checking numerical precision against the existing behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100