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Getting Started with Strangeworks


Creating Your Accountโ€‹

Start by signing up for a Strangeworks portal account. This account will allow you to browse your quantum jobs, view available QPUs, activate hardware, and retrieve your api key.

If you're an Enterprise customer, we've already created an organization workspace and a Strangeworks account for each member of your team. Please check your email for instructions on activating your account and accessing your workspace.

Quick tip: You and your team will run jobs and access resources for compute and managed applications in your organization's workspace. A personal workspace will also be automatically created with your new account. This is for personal use only and will not have activated resources.

After signing up, you will create a custom profile with details about your level of expertise, your areas of interest, and the types of projects you plan to work on. This helps us tailor the Strangeworks experience to provide you with the most relevant resources, projects, and collaboration opportunities.

*Note that we'll be turning off public signups soon.


Gathering Required Resourcesโ€‹

Finding Your Tokensโ€‹

To set up your local notebook to use the Strangeworks platform and run your first job, refer to the following items. All of these are located on the dashboard in your organization's workspace or within these docs.

Activating a Resourceโ€‹

As an Enterprise customer, we have already activated compute provider and managed application resources based on your organization's needs. If you would like to activate additional resources, follow the instructions below.

  • 1 In the Product Catalog, navigate to an activated compute provider
  • 2Click the "Add Resource" button, accept any terms and conditions if applicable, and activate another resource

Running Your First Jobsโ€‹

Authenticating with Strangeworksโ€‹

To begin running jobs on the Strangeworks platform, you may need to enable additional resource extensions. Below we provide instructions for installing and running a test job for Qiskit and AWS Braket to ensure your notebook is configured correctly for future jobs.

  • 1Download the Strangeworks SDK
  • 2Read the installation instructions for the required Python version and for setting up virtual environments
  • 3Pip install the Strangeworks SDK
  • 4Locate your Strangeworks API key on your workspace dashboard and copy the key
  • 5Follow the instructions to authenticate with the Strangeworks API using your API key

Quick tip: You will replace the "your-api-key" code snippet with your unique API key. If you begin experiencing Python execution errors while accessing the Strangeworks platform, check that the API key is accurate in your notebook and you have the required Python version installed.


Using The Portalโ€‹

The Strangeworks Portal is your gateway to the world of quantum computing. It serves as a central hub where you can access a wide range of quantum computing resources, tools, and services.

The portal is designed to make quantum computing more accessible, enabling you to run computations, manage your jobs, and collaborate with others in a user-friendly environment. Whether you're a seasoned quantum computing professional or just starting out in the field, the Strangeworks Portal provides you with the tools and resources you need to advance your quantum computing projects.

Learn more about how the portal works โ†’



Notebooks can help guide you in formulating scientific problems and running them on solvers.

The process begins by providing a high-level description of the scientific problem. The language model then interprets this description and generates a step-by-step guide in the form of a Jupyter notebook.

The generated notebook includes:

  • An introduction that explains the problem and the approach to solving it.
  • Detailed explanations of each step, including the relevant scientific concepts.
  • Python code that implements each step, using comments to explain what the code does.

This includes the code that formulates the problem in a way that can be solved by the available solvers. The notebook also includes the code that calls the solver and runs the problem on it.

Finally, the notebook would include the code to interpret the results returned by the solver. This could include visualizations, statistical analyses, and conclusions based on the results.

By using a large language model (LLM) to generate this notebook, the platform can provide a customized onboarding experience that helps the user understand how to use the platform to solve their specific scientific problem.



The Strangeworks Software Development Kit (SDK) serves as the underlying backbone for the management and execution of experimental projects in Python and other languages. It provides the essential tools and interfaces to interact with the Strangeworks platform and its various features.

Learn how to install the SDK โ†’

With the SDK, users can programmatically create, manage, and run experiments, manipulate datasets, and use the compute resources available on the Strangeworks platform. It enables developers to script their workflows, automate repetitive tasks, and integrate Strangeworks with their existing tools and systems.

Furthermore, the SDK supports a variety of quantum computing languages and frameworks, making it flexible and adaptable to different use cases and user preferences. This ensures a seamless user experience, bridging the gap between the high-level, user-friendly Strangeworks platform and the lower-level, technical aspects of quantum computing experimentation.


Experiments and Datasetsโ€‹

Experiments and Datasets in Strangeworks are crucial components for running and managing computational science experiments and research and development projects. They allow you to organize, run, and analyze experiments within the platform.

Experiments refer to the individual tests or calculations you conduct within your projects. You can set up parameters, input data, and choose the computational resources you want to use for each experiment. Once an experiment has run, the results are stored and can be analyzed within the platform.

Datasets, on the other hand, are collections of data that you use or generate in your experiments. These could be input data used in your calculations, or results data generated by your experiments. The Datasets feature allows you to manage and organize your data effectively. You can upload, download, share, and even version-control your datasets, ensuring that you and your team always have access to the correct, up-to-date data.

Together, the Experiments and Datasets features provide a comprehensive solution for managing your computational science projects, facilitating efficient and effective research and development.