pennylane

Maintained by k-dense-ai

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTo

Current version: Unknown
License: Apache-2.0 license
Network access: Unknown / not assessed
Review status: Not verified

Problem it solves

This catalog entry helps users find and evaluate pennylane for the task described by its available catalog summary. Confirm the exact scope in the linked original source when one is available.

When to use it

Consider pennylane when its available catalog summary matches the task at hand. When available, review the linked original source before use for precise instructions, requirements, and limitations.

pennylane is listed as an agent skill in RefHub. The listed maintainer is k-dense-ai. The available catalog summary is: Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTo When available, review the linked original source for exact usage instructions, required tools, and limitations.

Installation and updates

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Install command

npx skills add k-dense-ai/scientific-agent-skills --skill pennylane -y

Agent compatibility

No compatibility test has been recorded

Do not assume agent compatibility until documented test evidence is available.