To get started with quantum computing on AWS, enable Amazon Braket, choose a managed Jupyter notebook or local Python setup, and run a small circuit on a simulator before sending anything to a quantum processor (QPU). A Bell-state circuit is a useful first exercise: submit it as a quantum task, then inspect its measurement counts. Braket saves task results to an S3 bucket in your AWS account. Simulator runs avoid QPU usage charges, but AWS resources such as notebooks and simulators may still cost money.
How do I get started with Amazon Braket?
Amazon Braket is an AWS service for submitting work to quantum devices on demand. For a gate-based circuit, the submitted quantum task includes the circuit, measurement instructions, number of shots, and request metadata. (A shot is one run of the circuit.) Braket also supports analog Hamiltonian simulation tasks, which describe a register layout and control fields that vary over time and space.
You can define, submit, and monitor tasks in a Jupyter notebook with the Amazon Braket SDK or use the AWS console. The SDK provides a convenient layer over the Braket API and Boto3. After the selected device processes a task, Braket stores its results in an S3 bucket in your AWS account. See AWS’s getting started guide for account setup and service workflow.
Choose a notebook or local Python
A managed notebook is optional. A console-created Braket notebook is a Jupyter environment based on SageMaker AI notebook instances, with the Braket SDK and dependencies preloaded. Notebook compute is a separate AWS resource and can incur charges while it is running.
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Alternatively, install the SDK in a local Python environment with pip install amazon-braket-sdk. AWS also documents a PennyLane plugin package for users working with PennyLane. The AWS setup documentation covers both options.
Set up cost controls before experimenting
Braket has no upfront commitment for device access, but usage is billed. Keep track of the AWS resources involved—not just quantum tasks—including notebook compute, simulator use, S3 storage, and any other services your workflow uses. AWS recommends simulator testing before QPU runs, IAM controls for device access, and AWS Budgets alerts. The Braket cost-management guide explains available controls and their scope.
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How do I run my first quantum circuit on AWS?
Build a Bell-state circuit and run it on a simulator. The circuit prepares two qubits in a linked state whose ideal measurement outcomes are 00 and 11. AWS’s “Building your first circuit” example shows how to create the circuit, execute it, and inspect counts. With a finite number of shots, the two counts should be roughly balanced, not necessarily identical.
- Import the SDK modules. Use the Braket SDK in your notebook or local Python environment.
- Choose a simulator device. Start with a local simulator for quick small-circuit checks, or select an on-demand simulator such as SV1 when appropriate.
- Build the Bell circuit. Follow the AWS example to create the two-qubit circuit and its measurement instructions.
- Submit the task. Specify the number of shots and call the device to run the circuit. Braket returns a task reference you can monitor.
- Read the result counts. Inspect the measurement results for the observed frequencies of
00and11. Small differences are expected from shot noise.
The same core SDK pattern applies to other circuits: import the SDK, select a device, instantiate a circuit, run it, and collect its results. A simulator run is a useful way to catch code and configuration errors before a QPU task, without incurring QPU usage charges. It does not eliminate possible costs for simulator compute, notebooks, storage, or other AWS services.
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Yes. Braket offers local and on-demand simulators as well as QPUs; the right choice depends on what you are learning or testing. A simulator helps with circuit development and debugging, while a QPU run is for experimenting with physical quantum hardware. Simulator capacities in AWS documentation describe published capabilities, not guaranteed runtime or performance on every host or circuit.
| Option | Best fit | AWS-documented capacity | Practical consideration |
|---|---|---|---|
| Local state-vector simulator | Rapid prototyping and small-circuit debugging | Up to 25 qubits, depending on host hardware | Runs on your local machine; actual capacity and speed depend on that machine. |
| SV1 on-demand state-vector simulator | Managed simulation for circuits beyond local resources | Up to 34 qubits | AWS says a dense 34-qubit circuit of depth 34 may take around one to two hours, depending on gates and other factors; simulator use can incur charges. |
| DM1 density-matrix simulator | Simulation using a density-matrix method | Up to 17 qubits | Use when its simulation method fits the task; performance and costs depend on the workload. |
| QPU | Experiments on physical quantum hardware | Varies by device | Supported operations, availability windows, queues, Regions, and costs vary; check the live device details before submitting. |
These capacities come from AWS’s simulator documentation. AWS recommends verifying work on a simulator before using a QPU, but simulation is not automatically cheaper overall: simulator tasks and supporting AWS services can still generate charges.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I choose a Braket device?
Choose according to the task’s purpose and requirements rather than treating a QPU as the automatic next step. Check the circuit size and simulator method you need, the device’s supported gates and result types, and whether the goal is debugging, noise modeling, or a physical-hardware experiment. For a QPU, also review the provider’s technology, current availability window, queue, Region, and pricing.
AWS’s device guide lists QPU providers including AQT, IonQ, IQM, QuEra, and Rigetti. Device inventory and availability windows can change, so the console’s current status is not a permanent property of a device. Consult the Braket device guide and current device details before each hardware submission.
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Your working Region does not necessarily have to match the QPU’s Region: the SDK can submit to a QPU in another Region by creating a session for the device Region. Check regional endpoints and current device availability as part of setup.
What can AWS charge for, and how do I avoid surprises?
Potential costs include quantum task usage and supporting AWS resources. A managed notebook has its own compute charges; simulator use, storage, Hybrid Job EC2 instances, and other AWS services may also affect the bill. Check current task, notebook-instance, and device pricing before running work, because rates and the device inventory can change.
AWS offers near-real-time cost estimates and optional per-device spending limits for QPU tasks. Those limits do not cover simulator tasks, managed notebooks, Hybrid Job EC2 instance costs, or Braket Direct reservations. Estimates can differ from actual charges and may not include every discount, credit, or cost from other AWS services. See the AWS cost-management documentation for details.
Quick Recap
- Start with a simulator and limit the number of shots while debugging.
- Use IAM to control who can access devices, and set AWS Budgets alerts for account spending.
- When checking quantum-task charges in the console, review every Region: the console lists tasks only for the Region currently selected.
- Review notebook and other resource usage as well as QPU spending limits; a QPU limit is not a cap on all Braket-related charges.
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