qiskit

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

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---
name: qiskit
description: IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
license: Apache-2.0 license
metadata:
    skill-author: K-Dense Inc.
---

# Qiskit

## Overv

How to Use

Recommended: Install to project (local)

mkdir -p .claude/skills
curl -o .claude/skills/qiskit.md \
  https://raw.githubusercontent.com/K-Dense-AI/claude-scientific-skills/main/scientific-skills/qiskit/SKILL.md

Skill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.

Alternative: Clone full repo

git clone https://github.com/K-Dense-AI/claude-scientific-skills

Then reference at scientific-skills/qiskit/SKILL.md

Related Skills

Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.

by sickn33 (Antigravity) · antigravity-awesome-skills

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

by K-Dense-AI · claude-scientific-skills

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 PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

pennylanepennylaneml

by K-Dense-AI · claude-scientific-skills

Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.

by K-Dense-AI · claude-scientific-skills