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braket-executor skill

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This skill helps orchestrate quantum circuit execution across multiple vendors using Braket, enabling hybrid quantum-classical workflows and cost-aware job

npx playbooks add skill a5c-ai/babysitter --skill braket-executor

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: braket-executor
description: Amazon Braket integration skill for multi-vendor quantum hardware access and hybrid workflows
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
metadata:
  specialization: quantum-computing
  domain: science
  category: quantum-framework
  phase: 6
---

# Braket Executor

## Purpose

Provides expert guidance on executing quantum circuits across multiple hardware vendors using Amazon Braket, enabling hybrid quantum-classical workflows in the AWS ecosystem.

## Capabilities

- Circuit execution on IonQ, Rigetti, OQC hardware
- Hybrid job execution with classical processing
- Quantum annealing on D-Wave
- Local simulator execution
- Cost estimation and job management
- Result storage in S3
- Batch job submission
- Noise simulation

## Usage Guidelines

1. **Device Selection**: Choose appropriate hardware based on circuit requirements and availability
2. **Circuit Translation**: Use Braket SDK to build or import circuits from other frameworks
3. **Hybrid Jobs**: Configure containerized hybrid workflows with classical compute
4. **Cost Management**: Monitor and estimate costs before job submission
5. **Result Retrieval**: Access results from S3 with proper error handling

## Tools/Libraries

- Amazon Braket SDK
- AWS Lambda
- Amazon S3
- Braket Hybrid Jobs
- Braket Local Simulator

Overview

This skill integrates Amazon Braket for orchestrating multi-vendor quantum hardware access and hybrid quantum-classical workflows. It streamlines circuit execution across IonQ, Rigetti, OQC, and D-Wave, supports local simulation, and handles job lifecycle tasks like cost estimation and S3 result storage. Designed for use in agentic workflows, it enables deterministic, resumable orchestration of experiments and hybrid jobs.

How this skill works

The skill uses the Amazon Braket SDK to translate and submit circuits to selected backends, falling back to a local simulator when needed. It configures hybrid jobs that run containerized classical steps (e.g., optimization or preprocessing) alongside quantum tasks, tracks cost and runtime metadata, and stores outputs in S3 for reliable retrieval and post-processing. Batch submission, noise simulation, and basic job management operations are exposed for automation within agent workflows.

When to use it

  • Running the same circuit across multiple hardware vendors to compare performance and noise characteristics.
  • Building hybrid quantum-classical pipelines that require containerized classical compute steps combined with quantum tasks.
  • Estimating and controlling costs before submitting experiments to hardware backends.
  • Submitting large parameter sweeps or batch jobs that need resumable, deterministic orchestration.
  • Prototyping with local simulators before scaling to real quantum hardware or annealers.

Best practices

  • Select devices based on gate set, connectivity, and queue latency; validate with small test runs first.
  • Translate or transpile circuits via the Braket SDK to match target backend native operations.
  • Use hybrid jobs for tight classical-quantum loops and containerize dependencies to ensure reproducibility.
  • Estimate cost and runtime up front and tag jobs with metadata to simplify billing and tracking.
  • Store raw outputs and intermediate artifacts in S3 with versioned paths and implement retry logic for retrieval.

Example use cases

  • Benchmarking the same variational circuit across IonQ, Rigetti, and OQC to evaluate fidelity and runtime.
  • Running a hybrid VQE workflow where the optimizer runs in a container and quantum evaluations run on Braket hardware.
  • Executing annealing schedules on D-Wave for combinatorial optimization and aggregating results for classical post-processing.
  • Submitting batched parameter sweeps to the local simulator for rapid development, then replaying selected jobs on hardware.
  • Automating experiment submission, cost checks, and result archival in S3 as part of an agent-managed CI pipeline.

FAQ

Which providers are supported?

IonQ, Rigetti, OQC for gate-based devices and D-Wave for annealing are supported, plus a Braket local simulator.

How are hybrid jobs configured?

Hybrid jobs run containerized classical steps via Braket Hybrid Jobs; provide a container image and entrypoint, then link quantum tasks and data in S3.