tensorflow/quantum

Remove use of finite different in Adjoint gradient

Open

#620 opened on Aug 19, 2021

View on GitHubContributor guide
 (0 comments) (0 reactions) (0 assignees)Python (662 forks)github user discovery
area/corehelp wantedkind/feature-requestskill-level/intermediate

Repository metrics

Stars
 (2,180 stars)
PR merge metrics
 (Avg merge 13d 10h) (2 merged PRs in 30d)

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

Research direction
Find the current finite difference implementation in the Adjoint gradient C++ code and derive analytic gradient formulas for gates. Then replace the finite difference with analytic expressions.
Tech stack
pythoncpp
Domain
machine learning
Issue type
Refactor
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
C++Pythonquantum computing basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35