Please take a look at our contribution guidelines before getting started. Thank you!
The Dash template is intended for demos that would benefit from a user interface. This user interface could include settings to run and customize the problem, an interactive graphical element, or tables/charts to compare different solutions. This template is also useful for demos that are intended for a general audience, as it is more approachable for those without a technical background.
Describe your demo and specify what it is demonstrating. Consider the following questions:
- Is it a canonical problem or a real-world application?
- Does it belong to a particular domain such as material simulation or logistics?
- What level of Ocean proficiency does it target: beginner, advanced?
A clear description allows us to properly categorize your demo.
Please include a screenshot of your demo below.
You can run this example without installation in cloud-based IDEs that support the Development Containers specification (aka "devcontainers") such as GitHub Codespaces.
For development environments that do not support devcontainers, install requirements:
pip install -r requirements.txtIf you are cloning the repo to your local system, working in a virtual environment is recommended.
Your development environment should be configured to access the Leap™ Quantum Cloud Service. You can see information about supported IDEs and authorizing access to your Leap account here.
Run the following terminal command to start the Dash app:
python app.pyAccess the user interface with your browser at http://127.0.0.1:8050/.
The demo program opens an interface where you can configure problems and submit these problems to a solver.
Configuration options can be found in the demo_configs.py file.
Note
If you plan on editing any files while the app is running,
please run the app with the --debug command-line argument for live reloads and easier debugging:
python app.py --debug
Give an overview of the problem you are solving in this demo.
Objectives: define the goal this example attempts to accomplish by minimizing or maximizing certain aspects of the problem. For example, a production-line optimization might attempt to minimize the time to produce all of the products.
Constraints: aspects of the problem, with limited or no flexibility, that must be satisfied for solutions to be considered feasible. For example, a production-line optimization might have a limitation that Machine A can only bend 10 parts per hour.
The clearer your model is presented here, the more useful it will be to others. For a strong example of this section, see here.
List and define the parameters used in your model.
List and define (including type: e.g., "binary" or "integer") the variables solved for in your model.
List and define any combinations of variables used for easier representations of the models.
Mathematical formulation of the objective described in the previous section using the listed parameters, variables, etc.
Mathematical formulation of the constraints described in the previous section using the listed parameters, variables, etc.
A general overview of how the code works.
Include any notable parts of the code implementation:
- Talk about unusual or potentially difficult parts of the code
- Explain a code decision
- Explain how parameters were tuned
Note: there is no need to repeat everything that is already well-documented in the code.
A. Person (YEAR), "An Article Title that Helped Formulate the Problem". Link Title
Released under the Apache License 2.0. See LICENSE file.