Package: amazon-braket-sdk-kshitijc
Amazon Braket Python SDKThe Amazon Braket Python SDK is an open source library that provides a framework that you can use to interact with quantum computing hardware devices through Amazon Braket.PrerequisitesBefore you begin working with the Amazon Braket SDK, make sure that you've installed or configured the following prerequisites.Python 3.7.2 or greaterDownload and install Python 3.7.2 or greater fromPython.org.GitInstall Git fromhttps://git-scm.com/downloads. Installation instructions are provided on the download page.IAM user or role with required permissionsAs a managed service, Amazon Braket performs operations on your behalf on the AWS hardware that is managed by Amazon Braket. Amazon Braket can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.The Braket Python SDK should not require any additional permissions aside from what is required for using Braket. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.To learn more about IAM user, roles, and policies, seeAdding and Removing IAM Identity Permissions.Boto3 and setting up AWS credentialsFollow the installationinstructionsfor Boto3 and setting up AWS credentials.Note:Make sure that your AWS region is set to one supported by Amazon Braket. You can check this in your AWS configuration file, which is located by default at~/.aws/config.Configure your AWS account with the resources necessary for Amazon BraketIf you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using theAWS console.Installing the Amazon Braket Python SDKThe Amazon Braket Python SDK can be installed with pip as follows:pipinstallamazon-braket-sdkYou can also install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/aws/amazon-braket-sdk-python.gitcdamazon-braket-sdk-python
pipinstall.Check the version you have installedYou can view the version of the amazon-braket-sdk you have installed by using the following command:pipshowamazon-braket-sdkYou can also check your version ofamazon-braket-sdkfrom within Python:>>> import braket._sdk as braket_sdk
>>> braket_sdk.__version__Updating the Amazon Braket Python SDKYou can update the version of the amazon-braket-sdk you have installed by using the following command:pipinstallamazon-braket-sdk--upgrade--upgrade-strategyeagerUsageRunning a circuit on an AWS simulatorimportboto3frombraket.awsimportAwsDevicefrombraket.circuitsimportCircuitdevice=AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")bell=Circuit().h(0).cnot(0,1)task=device.run(bell,shots=100)print(task.result().measurement_counts)The code sample imports the Amazon Braket framework, then defines the device to use (the SV1 AWS simulator). It then creates a Bell Pair circuit, executes the circuit on the simulator and prints the results of the job. This example can be found in../examples/bell.py.Running multiple tasks at onceMany quantum algorithms need to run multiple independent circuits, and submitting the circuits in parallel can be faster than submitting them one at a time. In particular, parallel task processing provides a significant speed up when using simulator devices. The following example shows how to run a batch of tasks on SV1:circuits=[bellfor_inrange(5)]batch=device.run_batch(circuits,shots=100)print(batch.results()[0].measurement_counts)# The result of the first task in the batchRunning a hybrid jobfrombraket.awsimportAwsQuantumJobjob=AwsQuantumJob.create(device="arn:aws:braket:::device/quantum-simulator/amazon/sv1",source_module="job.py",entry_point="job:run_job",wait_until_complete=True,)print(job.result())whererun_jobis a function in the filejob.py.The code sample imports the Amazon Braket framework, then creates a hybrid job with the entry point being therun_jobfunction. The hybrid job creates quantum tasks against the SV1 AWS Simulator. The job runs synchronously, and prints logs until it completes. The complete example can be found in../examples/job.py.Available SimulatorsAmazon Braket provides access to two types of simulators: fully managed simulators, available through the Amazon Braket service, and the local simulators that are part of the Amazon Braket SDK.Fully managed simulators offer high-performance circuit simulations. These simulators can handle circuits larger than circuits that run on quantum hardware. For example, the SV1 state vector simulator shown in the previous examples requires approximately 1 or 2 hours to complete a 34-qubit, dense, and square circuit (circuit depth = 34), depending on the type of gates used and other factors.The Amazon Braket Python SDK includes an implementation of quantum simulators that can run circuits on your local, classic hardware. For example the braket_sv local simulator is well suited for rapid prototyping on small circuits up to 25 qubits, depending on the hardware specifications of your Braket notebook instance or your local environment. An example of how to execute the task locally is included in the repository../examples/local_bell.py.For a list of available simulators and their features, consult theAmazon Braket Developer Guide.Debugging logsTasks sent to QPUs don't always run right away. To view task status, you can enable debugging logs. An example of how to enable these logs is included in repo:../examples/debug_bell.py. This example enables task logging so that status updates are continuously printed to the terminal after a quantum task is executed. The logs can also be configured to save to a file or output to another stream. You can use the debugging example to get information on the tasks you submit, such as the current status, so that you know when your task completes.Running a Quantum Algorithm on a Quantum ComputerWith Amazon Braket, you can run your quantum circuit on a physical quantum computer.The following example executes the same Bell Pair example described to validate your configuration on a Rigetti quantum computer.importboto3frombraket.circuitsimportCircuitfrombraket.awsimportAwsDevicedevice=AwsDevice("arn:aws:braket:::device/qpu/rigetti/Aspen-8")bell=Circuit().h(0).cnot(0,1)task=device.run(bell)print(task.result().measurement_counts)When you execute your task, Amazon Braket polls for a result. By default, Braket polls for 5 days; however, it is possible to change this by modifying thepoll_timeout_secondsparameter inAwsDevice.run, as in the example below. Keep in mind that if your polling timeout is too short, results may not be returned within the polling time, such as when a QPU is unavailable, and a local timeout error is returned. You can always restart the polling by usingtask.result().task=device.run(bell,poll_timeout_seconds=86400)# 1 dayprint(task.result().measurement_counts)To select a quantum hardware device, specify its ARN as the value of thedevice_arnargument. A list of available quantum devices and their features can be found in theAmazon Braket Developer Guide.ImportantTasks may not run immediately on the QPU. The QPUs only execute tasks during execution windows. To find their execution windows, please refer to theAWS consolein the "Devices" tab.Sample NotebooksSample Jupyter notebooks can be found in theamazon-braket-examplesrepo.Braket Python SDK API Reference DocumentationThe API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-sdk-pythondirectory. Then, run the following command to generate the HTML documentation files:pipinstalltox
tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-sdk-python/build/documentation/html/index.htmlTestingThis repository has both unit and integration tests.To run the tests, make sure to install test dependencies first:pipinstall-e"amazon-braket-sdk-python[test]"Unit TestsTo run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxIntegration TestsFirst, configure a profile to use your account to interact with AWS. To learn more, seeConfigure AWS CLI.After you create a profile, use the following command to set theAWS_PROFILEso that all future commands can access your AWS account and resources.exportAWS_PROFILE=YOUR_PROFILE_NAMETo run the integration tests for local jobs, you need to have Docker installed and running. To install Docker follow these instructions:Install DockerRun the tests:tox-einteg-testsAs with unit tests, you can also pass in various pytest arguments:tox-einteg-tests--your-argumentsSupportIssues and Bug ReportsIf you encounter bugs or face issues while using the SDK, please let us know by posting
the issue on ourGithub issue tracker.For issues with the Amazon Braket service in general, please use theDeveloper Forum.Feedback and Feature RequestsIf you have feedback or features that you would like to see on Amazon Braket, we would love to hear from you!Github issuesis our preferred mechanism for collecting feedback and feature requests, allowing other users
to engage in the conversation, and +1 issues to help drive priority.LicenseThis project is licensed under the Apache-2.0 License.