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discopy-categorical-computing

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Master diagrams and quantum circuits with Discopy.

Education & Research#category theory#discopy#QNLP#string diagrams#quantum circuits
Authormanutej
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Discopy enables researchers and developers to design, reason about, and evaluate complex composed workflows using string diagrams, tensor networks, and quantum circuits within a single formalism.

Core Features & Use Cases

  • Compositionally safe design: build linear pipelines, parallel processes, and braidings with guaranteed type-correctness.
  • Multimodal backends & semantics: interpret diagrams as tensors (NumPy, PyTorch, JAX, TensorFlow) or quantum circuits, or symbolic representations for reasoning.
  • Education & prototyping: ideal for teaching category theory concepts, validating experiments, and rapidly prototyping QNLP and quantum computation ideas.

Quick Start

Install the package and import the core primitives, build a tiny diagram f: X → Y and g: Y → Z, then evaluate with a basic matrix Functor.

Dependency Matrix

Required Modules

None required

Components

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: discopy-categorical-computing
Download link: https://github.com/manutej/fstar-labs/archive/main.zip#discopy-categorical-computing

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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