gradient-check
CommunityStabilize gradients in hybrid quantum training.
Authornecatiincekara
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Diagnoses vanishing or exploding gradients in the hybrid quantum-classical training path, enabling targeted stabilization to prevent stalls or collapses during optimization.
Core Features & Use Cases
- Gradient diagnostics: traces gradient flow from loss through classical and quantum parameters, flags zero or unstable gradients, and identifies AMP boundary issues.
- Scenario coverage: applicable to V6 collapse, trainable quantum instability, and AMP-related failures across training runs.
- Use Case: when a training run stalls due to low gradient magnitudes, run this skill to surface bottlenecks and actionable fixes.
Quick Start
Run a diagnostic pass to compare gradient magnitudes between quantum and classical components and report stabilization actions.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: gradient-check Download link: https://github.com/necatiincekara/Quanvolutional-Neural-Network/archive/main.zip#gradient-check Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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