rb-benchmarker

Randomized benchmarking skill for gate fidelity characterization

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

rb-benchmarker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Randomized benchmarking skill for gate fidelity characterization

Teams using rb-benchmarker 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

$curl -o ~/.claude/skills/rb-benchmarker/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/quantum-computing/skills/rb-benchmarker/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/rb-benchmarker/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How rb-benchmarker Compares

Feature / Agent	rb-benchmarker	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?

Randomized benchmarking skill for gate fidelity characterization

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

# RB Benchmarker

## Purpose

Provides expert guidance on randomized benchmarking protocols for characterizing quantum gate fidelities and hardware performance.

## Capabilities

- Standard randomized benchmarking
- Interleaved randomized benchmarking
- Simultaneous RB for crosstalk
- Character benchmarking
- Cycle benchmarking
- Fidelity decay fitting
- SPAM error separation
- Confidence interval estimation

## Usage Guidelines

1. **Protocol Selection**: Choose RB variant based on characterization goals
2. **Sequence Generation**: Create random Clifford sequences of varying lengths
3. **Execution**: Run benchmarking experiments with sufficient statistics
4. **Fitting**: Analyze decay curves to extract fidelity parameters
5. **Reporting**: Generate comprehensive benchmarking reports

## Tools/Libraries

- Qiskit Experiments
- Cirq
- True-Q
- PyGSTi
- SciPy

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