Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Simulates a photonic quantum computer that uses optical fiber storage and linear optics for computation. Calculates the quantum kernel similarity between two data vectors by encoding them into optical phases, passing them through fiber simulations with attenuation and phase noise, then interfering the results. Capped at 8 modes with strict input validation.
Includes physics-based attenuation and phase noise, making simulations more realistic than idealized quantum kernel implementations.
When to use it
- Comparing similarity between feature vectors in a machine learning pipeline
- Prototyping quantum kernel methods without physical hardware
- Testing optical quantum circuit behavior in research
- Exploring photonic computing concepts in coursework or experiments
- Benchmarking quantum similarity metrics for classification tasks
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Calculate the quantum kernel similarity between vectors [0.5, 0.3, 0.8] and [0.1, 0.7, 0.4]
- 1Validates input vector dimensions and checks values are within allowed bounds
- 2Encodes each vector as optical phases across fiber modes
- 3Simulates fiber propagation with attenuation and phase noise applied
- 4Interferes the two encoded optical states using linear optics
- 5Returns the computed scalar kernel similarity value
A scalar similarity score representing the quantum kernel between the two input vectors