Computes and manipulates string diagrams in monoidal categories with Python backends and rendering.
npx skills add https://github.com/plurigrid/asi --skill discopy
Please help me install this Agent Skill. Skill: discopy Source: https://github.com/plurigrid/asi/tree/main/skills/discopy Command: npx skills add https://github.com/plurigrid/asi --skill discopy
DisCoPy provides a Python library to compute with string diagrams for monoidal categories, along with operads, quantum circuits, and tensor networks.
Just run: just discopy-demo
High-intent search queries and answers about installing and using this skill.
DisCoPy computes string diagrams in monoidal categories through a complete class hierarchy (Category, Ob, Arrow, Box, Diagram) with composition operators (then, tensor, dagger) and graphical rendering. It supports operads, colored types, and external integrations like pytket and PennyLane for backends including NumPy, JAX, and PyTorch.
Yes. DisCoPy provides quantum circuit construction, gate definitions, ZX-calculus support, and simulation capabilities. It integrates with pytket and PennyLane backends, enabling circuit composition, visualization, and execution across NumPy, JAX, and PyTorch compute frameworks.
DisCoPy implements operads, colored operads, free operads, and algebras alongside tensor network construction through its monoidal category framework. Use its composition operators and external backends to manipulate, visualize, and compute over operadic and tensor structures.
Yes. DisCoPy supports QNLP through DisCoCat mappings that bridge categorical linguistics and quantum circuits. You can build, compose, and visualize quantum NLP diagrams with ZX-calculus and simulate them using integrated backends.
DisCoPy integrates with pytket for quantum compilation, PennyLane for variational circuits, and PyZX for ZX-calculus. It supports NumPy, JAX, and PyTorch backends, allowing seamless composition with existing quantum and tensor workflows.
DisCoPy's performance depends on backend choice (NumPy, JAX, PyTorch) and diagram complexity. For production-scale tensor networks, performance tuning and backend selection become critical; verification against external libraries like PyZX or pytket is recommended for validation.
manutejDesign and interpret string diagrams, quantum circuits, and tensor networks with category theory.
tassiovaleConstruct and train quantum circuits for machine learning workflows.
K-Dense-AIOptimize quantum circuits with automatic differentiation across simulators and hardware.
HermeticOrmusImplement categorical quantum NLP models using DisCoPy string diagrams.
