Can I use this for quantum computing?

Yes, it supports mapping diagrams to quantum circuits and evaluating them through tensor contraction or standard circuit execution using DiscoPy's quantum module.

Does it support visualization?

Yes, it includes tools for drawing string diagrams and equations with custom color schemes, facilitating the visual debugging of categorical structures.

What is the primary use of the DiscoPy Operads skill?

It allows Claude to use the DiscoPy Python library to model complex systems using string diagrams, operadic composition, and category theory principles.

How does it relate to Active Inference?

The skill enables hierarchical Active Inference modeling where perception-action loops are structured as nested operads, allowing for rigorous mathematical treatment of AI behavior.

What is GF(3) trit conservation?

It is a mathematical check implemented in this skill that ensures compositional coherence by verifying that 'trits' (values in balanced ternary) are conserved during sequential composition.

DiscoPy Operads

Name: DiscoPy Operads
Author: plurigrid

byplurigrid

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データサイエンスとML

Facilitates computational category theory and operadic composition for advanced AI modeling and string diagram manipulation.

概要

This skill integrates DiscoPy into Claude, providing a robust environment for computing with string diagrams, monoidal categories, and colored operads. It specializes in skill composition through operadic laws, hierarchical Active Inference modeling, and GF(3) balanced ternary logic for compositional coherence. It is particularly useful for researchers and developers working on categorical semantics, quantum circuit design, linguistics, and structured AI agents who require a mathematically rigorous approach to problem-solving.

主な機能

Map diagrams to executable specifications, including Python functions and quantum circuits
Implement hierarchical Active Inference loops using nested operadic structures
2 GitHub stars
Construct and manipulate string diagrams in Python using monoidal category laws
Generate deterministic box coloring and high-quality visualizations for diagrams
Verify GF(3) balanced trit conservation for compositional coherence checks

ユースケース

Modeling multi-input workflows and functional pipelines using category theory and wiring diagrams
Designing hierarchical perception-action loops for complex software agents and robotic control
Prototyping and verifying quantum circuits and linguistic structures through categorical semantics

概要

主な機能

Map diagrams to executable specifications, including Python functions and quantum circuits
Implement hierarchical Active Inference loops using nested operadic structures
2 GitHub stars
Construct and manipulate string diagrams in Python using monoidal category laws
Generate deterministic box coloring and high-quality visualizations for diagrams
Verify GF(3) balanced trit conservation for compositional coherence checks

ユースケース

Modeling multi-input workflows and functional pipelines using category theory and wiring diagrams
Designing hierarchical perception-action loops for complex software agents and robotic control
Prototyping and verifying quantum circuits and linguistic structures through categorical semantics