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Amazon Braket

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Runs quantum workflows on AWS / Amazon Braket: circuits and algorithms, QPU/device and ARN discovery, LocalSimulator, tasks, program sets, Hybrid Jobs, S3 results, pricing, account setup, and spending limits. Applies to quantum programs and execution, AHS, OpenQASM, pulse control, reservations, notebooks, or Qiskit or PennyLane integrations.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Amazon Braket is a managed service for quantum computing. This skill provides guidance on using the Amazon Braket Python SDK to design, execute, and manage quantum circuits and algorithms. It covers the full lifecycle of quantum workloads, including device discovery, task submission, hybrid job orchestration, cost management, and integration with frameworks like Qiskit and PennyLane.

What it does

  • Discover and filter quantum processing units (QPUs) and simulators by region and capability.
  • Execute quantum circuits, analog Hamiltonian simulations, and OpenQASM programs.
  • Manage complex quantum-classical loops using Hybrid Jobs.
  • Implement cost guardrails through service-side spending limits.
  • Integrate with third-party quantum frameworks like Qiskit and PennyLane.
  • Configure and monitor quantum task execution and result retrieval via S3.

When to use it

  • When you need to run quantum algorithms on real hardware or simulators via AWS.
  • When you are setting up a new quantum development environment or notebook instance.
  • When you need to optimize costs for large-scale quantum experiments.
  • When you are building hybrid quantum-classical applications requiring managed orchestration.
  • When you need to verify device availability, connectivity, or calibration data.

Advantages

  • Provides a unified interface for diverse quantum hardware and simulators.
  • Enables cost control through explicit spending limits and tracking.
  • Supports advanced workflows like parameter sweeps and hybrid job management.
  • Facilitates seamless integration with existing Python-based quantum ecosystems.
  • Ensures consistent attribution and security practices for AWS-based quantum tasks.

Usage examples

“List all available online QPU devices in the us-east-1 region.”
The agent uses the Braket SDK to query the device fleet, filtering by status and type, and returns a list of available QPU ARNs.
“How do I set a spending limit for my Braket QPU tasks?”
The agent provides the steps to create a spending limit using the Braket API or SDK, ensuring the user understands the enforcement mechanism.
“Help me run a circuit on the IonQ Aria device.”
The agent guides you through selecting the device, preparing the circuit using the SDK, and submitting the task with appropriate attribution.
“How can I run multiple circuits efficiently in one task?”
The agent explains how to use Program Sets to bundle multiple independent programs into a single service-side task to optimize performance and costs.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/amazon-braket/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amazon-braket.zip -d .agents/skills

Documentation by AWS

<!-- markdownlint-disable MD013 -->

Amazon Braket

Primitives

The vocabulary of a Braket workflow, and which reference to open for each.

PrimitiveWhat it isRelated ReferencesOpen it when the request involves
DeviceA simulator or QPU, identified by a region-scoped ARNdevices.mdanything about a device: what exists, discovering or filtering the fleet, availability and status (online, offline, retired), which region a device lives in, ARNs, choosing a device for a workload, qubit count, connectivity or topology, native gates, fidelities, calibration data, queue depth, shot and gate limits, paradigm (gate-model vs analog Hamiltonian simulation), whether a device supports program sets, simulators and local emulators
ProgramThe workload/input — one executable (Circuit, AHS, OpenQASM). Which type is legal depends on the device's paradigm--
Quantum taskOne program + shots, run once (the atomic unit Braket meters)--
Task batchMany independent tasks — SDK-only fallback for when a program set does not fit; works on all devicesprogram-sets.mdsee the Program set row; also running multiple programs
Program setMany programs in one service-side task — preferred way to run multiple programs instead of task batchprogram-sets.mdrunning more than one program: parameter sweeps, scanning parameter values, task batches, run_batch, several circuits submitted together, attaching observables across programs, and minimizing per-task fees when many programs run, program sets. Also getting started with program sets
Hybrid jobManaged classical-quantum loop that orchestrates many taskshybrid-job.mdhybrid jobs: @hybrid_job, algorithm scripts and source modules, entry_point, embedded simulators, BYOC and custom container images, CUDA-Q, job execution roles, hyperparameters, checkpoints, and retrieving job results
Spending limitService-side hard cap that rejects QPU tasks — the only true enforcement (not SDK)spending-limit.mdcapping or enforcing spend: spending limits (create, update, delete, search), and cost guardrails
Cost trackingIn-session cost estimate (not enforcement)spending-limit.mdin-session cost tracking with Tracker
PricingCurrent Braket rates and pricing modelspricing.mdcurrent price or rate, simulator pricing, QPU per-task/per-shot pricing, Hybrid Job or reservation pricing, AWS Pricing API, bulk price list
ReservationExclusive device access for a booked window (Braket Direct)reservation.mdreservations: DirectReservation, reservation_arn, booking/cancelling a reservation, running tasks or hybrid jobs during a reservation window
NotebooksJupyter/JupyterLab notebook instances for exploring Braketnotebooks.mdcreating or managing a Braket notebook instance, Jupyter, getting-started environment for Braket
Pulse control / gate calibrationsNative gate calibrations and custom pulse sequences on supported QPUspulse-control.mdpulse control, gate calibrations, gate_calibrations, refresh_gate_calibrations, gate_definitions
Experimental capabilitiesOpt-in device features outside the published specificationsexperimental-capabilities.mdfeatures that not all QPUs support, EnableExperimentalCapability, experimental_capabilities
Library integrationsThird-party frameworks using Braket-owned providers or pluginsQiskit, PennyLaneQiskit or PennyLane workflows targeting Braket; provider/plugin behavior and Braket-specific compilation, submission, or result retrieval

Each reference carries the domain detail for its own area — field paths, key names, API shapes, and billing models.

Additional notes:

  • Setup — first-time account setup, prerequisites, roles, notebooks, and the results S3 bucket are covered in setup.md.

Critical Rules

These rules apply to every Braket request, whatever it involves.

  1. Use credentials with IAM-administrative permissions only for setup actions, such as creating IAM roles and policies, creating or updating spending limits, and creating S3 buckets or other resources. After setup, use workload credentials with permissions scoped to the workflow.

  2. The Amazon Braket Python SDK (pip install amazon-braket-sdk, imported as braket) is the primary entry point. Prefer it for every operation including Braket API operations, and understand what it covers by reading the docs or inspecting the SDK's modules locally.

    • Use local execution tools for running the Braket SDK, such as shell with python3 -c "<code>".

  3. The AWS MCP server is recommended for executing any other AWS API calls in this skill, especially operations not present in the Python SDK, although not required. Note: the AWS MCP's run_script tool executes code in a minimal sandbox without Braket libraries, so prefer using other tools for code execution, especially when using the Braket SDK.

  4. A small set of primitives composes every workflow — see Primitives for the vocabulary and the reference for each.

  5. Devices, quantum tasks, and hybrid jobs are region-scoped, so fan out across every Braket region whenever you use the API, CLI, or boto3 to search for resources. The SDK handles fanout for you where it can — AwsDevice.get_devices searches QPUs in all regions. Get the list of regions Braket supports from aws___get_regional_availability when the AWS MCP server is available, or from the supported devices and regions documentation. Resource ARNs containing a region may only be queried from that same region, otherwise you will see a ResourceNotFoundException.

  6. Open the matching reference before you write code or answer. Use the Primitives table to find and read references. Note: A request that asks for several things, e.g. executing a series of circuits and controlling the cost thereof, may require reading multiple reference files.

  7. Verify, never recall. Device ARNs and statuses, API signatures, supported features, and prices (and other values) all change and may post-date training data.

  8. Confirm a signature from documentation before you write code that calls it. This applies to the Braket SDK and to every third-party library that integrates with Braket. A guessed parameter or a guessed unit can produce a quantum task that runs, charges the customer, and returns a wrong answer.

    • For the Braket SDK, read the SDK reference docs.

    • For a third-party library, open its matching library reference from the Primitives table and verify signatures against the linked provider or plugin documentation.

    If no available tool reaches the documentation, read the signature from the installed package with shell:

    # subpackages: ahs, circuits, pulse, program_sets, ...
    PAGER=cat python -c "import braket; help(braket)"
    # names: DrivingField, AtomArrangement, ...
    python -c "import braket.ahs; print(dir(braket.ahs))"
    python -c "import inspect; from braket.ahs import DrivingField; \
    print(inspect.signature(DrivingField.from_lists)); print(inspect.getdoc(DrivingField.from_lists))"
  9. Attribute skill usage. In every Python script that uses the Braket SDK, insert this block before any Braket SDK code, including import braket:

    import botocore
    
    def _braket_attribution(session):
        session.user_agent_extra = (
            f"{session.user_agent_extra} AWSSkill-Braket/1.0.0"
        ).strip()
    
    botocore.register_initializer(_braket_attribution)

    If using the AWS CLI, prepend this skill's identifier to AWS_SDK_UA_APP_ID. The variable holds one value and other tools also write it, so always append the existing value instead of replacing it:

    # sh, bash, zsh, ksh, fish 3.1+
    AWS_SDK_UA_APP_ID="AWSSkill-Braket-$AWS_SDK_UA_APP_ID" aws braket ...
    # PowerShell — session-scoped
    if ($env:AWS_SDK_UA_APP_ID -notlike 'AWSSkill-Braket*') {
        $env:AWS_SDK_UA_APP_ID = "AWSSkill-Braket-$env:AWS_SDK_UA_APP_ID"
    }

    This marks AWS calls as originating from use with this skill. This is not needed when using AWS MCP tools as the AWS MCP automatically attributes during tool calls.

  10. Before the first QPU task on each device, search for an existing spending limit on that device ARN in its region; if none exists, create one with an amount the user confirms. Do not raise or delete a limit without an explicit user instruction. See spending-limit.md.

Guardrail — where this skill's own files live (MCP vs local install)

This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference:

  • Loaded through the AWS MCP retrieve_skill tool: The skill is not installed on the local filesystem. You MUST fetch each reference via retrieve_skill with the file parameter (e.g. file="references/devices.md"). Do NOT file_read these paths locally — they do not exist on disk.

  • Installed locally (e.g. ~/.kiro/skills/amazon-braket/, .kiro/skills/amazon-braket/, or ~/.claude/skills/amazon-braket/): Read files from the local skill directory using relative paths.

references/ is a sibling of this SKILL.md — resolve reference paths against that directory, not your working directory, and do not search the filesystem for them. If a skill tool returns this overview instead of the file you asked for, it did not fetch it: read it from that directory instead, and do not write code from memory because a reference read failed. This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory — do not cd before running a script that writes a relative artifact path.

Common mistakes

SymptomCauseFix
Treats "task", "batch", "job" as interchangeablePrimitive confusionTask = one run; batch = many parallel tasks; job = managed loop
Missing required parameter: filters on SearchQuantumTasks, SearchJobs, or SearchDevicesfilters is required on all three — only SearchSpendingLimits lets you omit itPass filters=[] (--filters '[]') for an unfiltered search. A populated filter needs name and values, plus operator on tasks and jobs; SearchDevices has no operator member
Missing required parameter: clientToken when using AWS MCP run_script toolCreateQuantumTask, CreateJob, CancelQuantumTask, CreateSpendingLimit, UpdateSpendingLimit require a clientToken for idempotency. The SDK, CLI, and boto3 generate one automatically; the run_script tool requires explicit passingPass clientToken=str(uuid.uuid4()) on APIs taking clientToken when calling through run_script. Otherwise, prefer the SDK for these operations when possible.
Builds program IR as JAQCDJAQCD is deprecated on Amazon BraketUse OpenQASM — see OpenQASM on Braket
Denies a feature or SDK construct existsTraining data outdatedVerify against the docs or GetDevice before saying it does not exist
Invents a class, method, or parameterWriting API names from memoryConfirm the signature first. If uncertain, say so rather than inventing

Security considerations

Braket workflows touch IAM, S3, and (for hybrid jobs) container execution.

For details, see the Amazon Braket security documentation.

Authoritative sources — prefer these over recalled details, since device ARNs, quotas, prices, and supported features change.

Official documentation (stable entry points — navigate/search from here)

For anything not covered here, search the documentation rather than guessing page slugs or recalling details: if the AWS MCP server is available, its aws___search_documentation tool can help; otherwise start from the Developer Guide or API Reference above and navigate.

GitHub

Files

references/hybrid-job.md

<!-- markdownlint-disable MD013 -->

Hybrid job

Key operations

OperationAmazon Braket Python SDKAWS CLIboto3 braket client
CreateAwsQuantumJob.create(...) or @hybrid_job decoratoraws braket create-job --region <region> --job-name <name> --role-arn <AmazonBraketJobsExecutionRole arn> --device-config '{"device":"<device>"}' --instance-config '{"instanceType":"<instance-type>","volumeSizeInGb":30}' --output-data-config '{"s3Path":"s3://<bucket>/<prefix>"}' --algorithm-specification '{"scriptModeConfig":{"entryPoint":"<module>:<function>","s3Uri":"s3://<bucket>/source.tar.gz","compressionType":"GZIP"}}' — all six flags are required, and you must package and upload source.tar.gz yourselfcreate_job
Status & metadataAwsQuantumJob.state(), .metadata()aws braket get-job --job-arn <arn> --region <region> --query '{status:status,failureReason:failureReason,spec:algorithmSpecification}'get_job
CancelAwsQuantumJob.cancel()aws braket cancel-job --job-arn <arn> --region <region>cancel_job
Search / list— no SDK methodaws braket search-jobs --filters '[]' --region <region> --query 'jobs[].{name:jobName,status:status,device:device}'search_jobs
Results / metrics / logsAwsQuantumJob.result(), .metrics(), .logs()—— (results in S3, metrics in CloudWatch)

Learn more about hybrid jobs. Choose a current instanceType from the supported instance types. If the user specifies a customer managed key, pass it as kmsKeyId in the output data config, and follow policy configurations in setup.md.

Submitting a job

Learn more about creating a hybrid job.

entry_point MUST be derived from source_module

entry_point is <module path inside the archive>:<function> — a colon between module and function, never a dot. The SDK archives source_module under its basename only, stripping every leading path component, so the module path is relative to that basename.

source_modulearchive containsentry_point
a/b/algorithm.py (a file)algorithm.py at rootalgorithm:main
a/b/hj_source (a directory)hj_source/algorithm_script.pyhj_source.algorithm_script:main

Two failure modes, both ModuleNotFoundError at container boot — do NOT do either:

  • ❌ bare module for a directory source_module: entry_point="algorithm_script:main" (missing the required hj_source. prefix)

  • ❌ full path in the module: entry_point="a.b.hj_source.algorithm_script:main" (leading a/b/ is stripped by the SDK)

Choosing a container

Embedded simulators

Embedded simulators run inside the job container instead of dispatching to a managed simulator or a QPU. Pass device="local:<provider>/<simulator>" and a matching image_uri=retrieve_image(Framework.<VARIANT>, region); the provider and simulator names come from the framework variant you select, not from a plugin's device name.

Learn more about embedded simulators.

Multi-GPU simulation with CUDA-Q

Whenever a simulation is GPU- or memory-bound — even if the user doesn't say "CUDA-Q" — for example a 30+ qubit state-vector simulation that runs out of memory on one GPU, consider using CUDA-Q with Hybrid Jobs. CUDA-Q's mgpu target pools the memory of several GPUs to hold one state vector. Refer to the notebook 6_Distributed_state_vector_simulations.ipynb for examples on how to use CUDA-Q with Hybrid Jobs for large simulations.

Learn more about using CUDA-Q on Amazon Braket.

Bring your own container (BYOC)

Bring your own container (BYOC) is the fully-custom path when the base images and framework variants don't fit your dependency stack.

Learn more about bringing your own container and running hybrid jobs in your own container.

Writing the algorithm script

Algorithm code MUST NOT hardcode device ARNs, buckets, or credentials — read them from the job environment (get_job_device_arn(), get_hyperparameters(), get_results_dir() from braket.jobs.environment_variables) so the same script runs unchanged across devices and regions.

To persist any data from the algorithm script, call save_job_result — never write result files by hand:

from braket.jobs import save_job_result
save_job_result({"counts": counts})

It writes results.json to the managed output directory and uploads it to the job's S3 output; read it back with load_job_result(). Do NOT open(...) a results path yourself or treat a bucket name as a local directory — the container has no such path and it raises FileNotFoundError.

Learn more about the algorithm script environment.

Debugging a failed job

@hybrid_job(local=True) or LocalQuantumJob.create(...) runs the container on your machine — use it to reproduce a failure before resubmitting.

Before using local mode, follow Create and debug a hybrid job with local mode for the required local environment setup, including Docker, and local-mode limitations.

Common mistakes
SymptomCauseFix
ModuleNotFoundError at container bootentry_point uses a dot, or its module path doesn't match the archive layoutDerive it from source_module per the procedure above
ModuleNotFoundError: __path__ attribute not foundAlgorithm script sits flat at the archive root (raw-API path only)Put it inside a named subdirectory; the SDK packages correctly
Missing dependency, e.g. scipyBase image lacks the packageAdd requirements.txt to the source module, or pick an image that includes it
Job fails before your code runsSource or results bucket is in a different region than the jobKeep both in the job's region
create rejects instance_count/instance_typeNot top-level kwargsUse InstanceConfig(instanceType=..., instanceCount=N); with instanceCount > 1 the algorithm must handle multiple hosts
Invented per-hyperparameter env varsAll hyperparameters are one JSON blob at AMZN_BRAKET_HP_FILERead them with get_hyperparameters()
create_job denied at PassRoleThe submitter's iam:PassRole doesn't cover the execution role's ARNCreate the role as described in setup.md, or grant the submitter iam:PassRole on the role's ARN
Tasks lose priority queueingcreate_quantum_task called through boto3 inside the algorithm scriptPass AMZN_BRAKET_JOB_TOKEN as jobToken (the SDK does this automatically)

References

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/quantum-computing-skills/amazon-braket · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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