ansatz-designer
Parameterized quantum circuit (ansatz) design skill for variational algorithms
Best use case
ansatz-designer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Parameterized quantum circuit (ansatz) design skill for variational algorithms
Teams using ansatz-designer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ansatz-designer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ansatz-designer Compares
| Feature / Agent | ansatz-designer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Parameterized quantum circuit (ansatz) design skill for variational algorithms
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Ansatz Designer ## Purpose Provides expert guidance on designing parameterized quantum circuits (ansatze) for variational quantum algorithms, balancing expressibility with trainability. ## Capabilities - Hardware-efficient ansatz generation - UCCSD ansatz construction - ADAPT-VQE ansatz building - Expressibility analysis - Barren plateau detection - Custom ansatz templates - Entanglement structure design - Layer depth optimization ## Usage Guidelines 1. **Problem Analysis**: Determine ansatz requirements based on target Hamiltonian 2. **Architecture Selection**: Choose between hardware-efficient and problem-inspired ansatze 3. **Expressibility Testing**: Evaluate ansatz capacity to represent target states 4. **Trainability Assessment**: Check for barren plateau indicators 5. **Hardware Adaptation**: Modify ansatz for target hardware connectivity ## Tools/Libraries - Qiskit Nature - PennyLane - Cirq - TensorFlow Quantum
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