| Name | qubots JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | A collaborative optimization framework. |
| upload_time | 2025-02-15 22:12:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
optimization
quantum
classical
cli
community
operation research
decision variables
qubo
|
| VCS |
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Requests
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|
# Qubots: A Collaborative Optimization Framework
[](https://test.pypi.org/project/qubots/)
[](https://github.com/leonidas1312/qubots/actions)
[](./LICENSE)
[](https://github.com/leonidas1312/qubots/issues)
[](https://github.com/leonidas1312/qubots/network)
Qubots is a Python library that turns optimization problems and optimization algorithms (optimizers) into shareable, modular “qubots”. Whether you’re developing a new optimizer or formulating a complex problem, qubots makes it easy to package, share, and run your work. Through our central hub, Rastion, you can browse, download, and contribute to a growing ecosystem of GitHub repositories dedicated to cutting-edge optimization – spanning classical, quantum, and hybrid methods.
## Rastion
Rastion serves as the central repository hub for all qubots-related projects. On the Rastion page you’ll find detailed documentation, guides on usage, contribution instructions, and real-world use cases that demonstrate how to combine and chain qubots for advanced optimization workflows. Visit the demo page here: https://repo-bloom-portal.lovable.app/
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Getting Started](#getting-started)
- [Technical Overview](#technical-overview)
- [Base Classes](#base-classes)
- [Dynamic Qubot Loading: AutoProblem & AutoOptimizer](#dynamic-qubot-loading-autoproblem--autooptimizer)
- [Chaining and Pipelines](#chaining-and-pipelines)
- [CLI Tools](#cli-tools)
- [Use Cases & Examples](#use-cases--examples)
- [Contributing](#contributing)
- [License](#license)
## Features
- **Modular Design**: Formulate your optimization problems and solvers as independent “qubots” that follow a simple interface.
- **Dynamic Loading**: Use the built-in `AutoProblem` and `AutoOptimizer` classes to dynamically load and execute GitHub repositories containing your qubots.
- **Hybrid Optimization**: Seamlessly combine quantum and classical optimizers using our quantum-classical pipeline.
- **CLI Integration**: Manage repositories (create, update, delete, push) via command-line tools for smooth collaboration.
- **Extensive Examples**: Learn from a rich collection of examples covering classical optimization (e.g., Particle Swarm, Tabu Search), quantum approaches (e.g., QAOA, VQE), and continuous problem solvers.
## Installation
Qubots is available on PyPI. To install, simply run:
```bash
pip install qubots
```
For full documentation and guides, please visit the Rastion Hub demo page https://repo-bloom-portal.lovable.app/docs .
## Getting Started
Here’s a brief example showing how to load a problem and a solver from the Rastion hub, then run the optimization:
```python
from qubots.auto_problem import AutoProblem
from qubots.auto_optimizer import AutoOptimizer
# Load the portfolio optimization problem from the Rastion GitHub repository.
problem = AutoProblem.from_repo("Rastion/portfolio-optimization")
# Load the Particle Swarm Optimizer with overridden parameters.
optimizer = AutoOptimizer.from_repo(
"Rastion/particle-swarm",
override_params={"swarm_size": 60, "max_iters": 500}
)
# Run the optimization and print results.
best_solution, best_cost = optimizer.optimize(problem)
print("Portfolio Optimization with PSO")
print("Best Solution:", best_solution)
print("Best Cost:", best_cost)
```
This simple workflow demonstrates how qubots allow you to plug and play various optimization modules with minimal boilerplate.
## Technical Overview
### Base Classes
At the core of qubots are two abstract base classes:
- **BaseProblem**
- Defines the interface for any optimization problem. Every problem qubot must implement:
- `evaluate_solution(solution) -> float`
- Computes the objective value (or cost) for a given candidate solution.
- `random_solution() (optional)`
- Generates a random feasible solution for the problem.
- **BaseOptimizer**
- Provides the interface for optimization algorithms. Every optimizer qubot must implement:
- `optimize(problem, initial_solution=None, **kwargs) -> (solution, cost)`
- Runs the optimization on a given problem, optionally starting from an initial solution.
These interfaces ensure that every qubot—whether problem or solver—can be seamlessly interchanged and composed.
### Dynamic Qubot Loading: AutoProblem & AutoOptimizer
To encourage modularity and collaboration, qubots can be dynamically loaded from GitHub repositories. This is accomplished using:
- **AutoProblem**
- Clones (or pulls) a repository from GitHub.
- Installs required packages (via `requirements.txt`).
- Reads a `problem_config.json` file that specifies an `entry_point` (formatted as `module:ClassName`) and default parameters.
- Dynamically imports and instantiates the problem qubot.
- **AutoOptimizer**
- Follows a similar process using a `solver_config.json` file.
- Installs required packages (via `requirements.txt`).
- Merges default parameters with any user-supplied `override_params`.
- Dynamically loads the optimizer class and returns an instance ready for use.
This design allows developers to share their work as self-contained GitHub repos that anyone can load, test, and incorporate into larger workflows. **Remote execution of python code files, including installing packages via requirements.txt, is not a good practice**. For this reason it is suggested to use Rastion & Qubots in a secure environment using `python -m venv` or `conda create --name my_rastion_env python=3.9`. Developing a sandbox environment & shareable object for qubots should definitely be in the future plans.
### Chaining and Pipelines
Qubots also supports more advanced patterns:
- **Independent Runs**: Use helper functions (in `optimizer_runner.py`) to run several optimizers independently on the same problem, then compare their results.
- **Chained Refinement**: Sequentially refine a solution by passing the output of one optimizer as the initial solution for the next.
- **Quantum-Classical Pipelines**: The `QuantumClassicalPipeline` class (and its helper function `create_quantum_classical_pipeline`) enables you to combine a quantum routine with a classical optimizer. For example, a quantum solver might generate a candidate solution which is then refined by a classical optimizer.
### CLI Tools
The package includes a set of command-line utilities (via the `rastion` script) to:
- **Create, Update, and Delete Repositories**: Easily manage GitHub repositories under the Rastion organization.
- **Push Solver and Problem Code**: Automate the process of packaging your qubot (along with its configuration and dependencies) and pushing it to GitHub.
- **Run Solvers Directly**: Load a solver and a problem from GitHub repositories and run the optimization in one command.
For example, to run a solver from the command line:
```bash
rastion run_solver Rastion/particle-swarm --problem-repo Rastion/max-cut
```
Most CLI tools need a verified github token to work. Currently this github token must be from a member of the Rastion org. We are working on a way to make Rastion automate this process (via a login option to the website).
## Examples
The `examples/` directory contains some examples and test cases, including:
- **Implemented tests**: Tests about the qubots library that have been tested to working locally.
- **Testing to do**: Tests that fail locally.
## Contributing
We welcome contributions to expand the qubots ecosystem! Here’s how you can get involved:
- **Report Issues**: If you encounter bugs or have feature suggestions, please open an issue on the GitHub repository.
- **Submit Pull Requests**: Follow the coding guidelines and ensure that your changes include tests and documentation updates as needed.
- **Share Your Qubots**: Have an innovative optimizer or an interesting problem formulation? Create a GitHub repo under the Rastion organization and share it via the Rastion hub.
- **Improve Documentation**: Enhance guides, tutorials, and examples to help others get started with qubots.
For more detailed contribution guidelines, please refer to our `CONTRIBUTING.md`.
## License
This project is licensed under the [Apache License 2.0](./LICENSE).
By leveraging the flexible design of qubots and the collaborative power of Rastion, you can rapidly prototype, share, and improve optimization solutions—be it for classical problems, quantum algorithms, or hybrid systems.
For more information, contact gleonidas303@gmail.com.
Raw data
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"keywords": "optimization, quantum, classical, cli, community, operation research, decision variables, QUBO",
"author": null,
"author_email": "\"Ioannis D. Leonidas\" <gleonidas303@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/7d/94/42e8fabf1ff453d57830655a4ce007b9488fdbc6d20425bc977107588911/qubots-0.1.2.tar.gz",
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"description": "# Qubots: A Collaborative Optimization Framework\n\n[](https://test.pypi.org/project/qubots/)\n[](https://github.com/leonidas1312/qubots/actions)\n[](./LICENSE)\n[](https://github.com/leonidas1312/qubots/issues)\n[](https://github.com/leonidas1312/qubots/network)\n\nQubots is a Python library that turns optimization problems and optimization algorithms (optimizers) into shareable, modular \u201cqubots\u201d. Whether you\u2019re developing a new optimizer or formulating a complex problem, qubots makes it easy to package, share, and run your work. Through our central hub, Rastion, you can browse, download, and contribute to a growing ecosystem of GitHub repositories dedicated to cutting-edge optimization \u2013 spanning classical, quantum, and hybrid methods.\n\n## Rastion\n\nRastion serves as the central repository hub for all qubots-related projects. On the Rastion page you\u2019ll find detailed documentation, guides on usage, contribution instructions, and real-world use cases that demonstrate how to combine and chain qubots for advanced optimization workflows. Visit the demo page here: https://repo-bloom-portal.lovable.app/\n\n## Table of Contents\n\n- [Features](#features)\n- [Installation](#installation)\n- [Getting Started](#getting-started)\n- [Technical Overview](#technical-overview)\n - [Base Classes](#base-classes)\n - [Dynamic Qubot Loading: AutoProblem & AutoOptimizer](#dynamic-qubot-loading-autoproblem--autooptimizer)\n - [Chaining and Pipelines](#chaining-and-pipelines)\n - [CLI Tools](#cli-tools)\n- [Use Cases & Examples](#use-cases--examples)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Features\n\n- **Modular Design**: Formulate your optimization problems and solvers as independent \u201cqubots\u201d that follow a simple interface.\n- **Dynamic Loading**: Use the built-in `AutoProblem` and `AutoOptimizer` classes to dynamically load and execute GitHub repositories containing your qubots.\n- **Hybrid Optimization**: Seamlessly combine quantum and classical optimizers using our quantum-classical pipeline.\n- **CLI Integration**: Manage repositories (create, update, delete, push) via command-line tools for smooth collaboration.\n- **Extensive Examples**: Learn from a rich collection of examples covering classical optimization (e.g., Particle Swarm, Tabu Search), quantum approaches (e.g., QAOA, VQE), and continuous problem solvers.\n\n## Installation\n\nQubots is available on PyPI. To install, simply run:\n\n```bash\npip install qubots\n```\n\nFor full documentation and guides, please visit the Rastion Hub demo page https://repo-bloom-portal.lovable.app/docs .\n\n## Getting Started\n\nHere\u2019s a brief example showing how to load a problem and a solver from the Rastion hub, then run the optimization:\n\n```python\nfrom qubots.auto_problem import AutoProblem\nfrom qubots.auto_optimizer import AutoOptimizer\n\n# Load the portfolio optimization problem from the Rastion GitHub repository.\nproblem = AutoProblem.from_repo(\"Rastion/portfolio-optimization\")\n\n# Load the Particle Swarm Optimizer with overridden parameters.\noptimizer = AutoOptimizer.from_repo(\n \"Rastion/particle-swarm\",\n override_params={\"swarm_size\": 60, \"max_iters\": 500}\n)\n\n# Run the optimization and print results.\nbest_solution, best_cost = optimizer.optimize(problem)\nprint(\"Portfolio Optimization with PSO\")\nprint(\"Best Solution:\", best_solution)\nprint(\"Best Cost:\", best_cost)\n```\n\nThis simple workflow demonstrates how qubots allow you to plug and play various optimization modules with minimal boilerplate.\n\n## Technical Overview\n\n### Base Classes\n\nAt the core of qubots are two abstract base classes:\n\n- **BaseProblem**\n - Defines the interface for any optimization problem. Every problem qubot must implement:\n - `evaluate_solution(solution) -> float`\n - Computes the objective value (or cost) for a given candidate solution.\n - `random_solution() (optional)`\n - Generates a random feasible solution for the problem.\n\n- **BaseOptimizer**\n - Provides the interface for optimization algorithms. Every optimizer qubot must implement:\n - `optimize(problem, initial_solution=None, **kwargs) -> (solution, cost)`\n - Runs the optimization on a given problem, optionally starting from an initial solution.\n\nThese interfaces ensure that every qubot\u2014whether problem or solver\u2014can be seamlessly interchanged and composed.\n\n### Dynamic Qubot Loading: AutoProblem & AutoOptimizer\n\nTo encourage modularity and collaboration, qubots can be dynamically loaded from GitHub repositories. This is accomplished using:\n\n- **AutoProblem**\n - Clones (or pulls) a repository from GitHub.\n - Installs required packages (via `requirements.txt`).\n - Reads a `problem_config.json` file that specifies an `entry_point` (formatted as `module:ClassName`) and default parameters.\n - Dynamically imports and instantiates the problem qubot.\n\n- **AutoOptimizer**\n - Follows a similar process using a `solver_config.json` file.\n - Installs required packages (via `requirements.txt`).\n - Merges default parameters with any user-supplied `override_params`.\n - Dynamically loads the optimizer class and returns an instance ready for use.\n\nThis design allows developers to share their work as self-contained GitHub repos that anyone can load, test, and incorporate into larger workflows. **Remote execution of python code files, including installing packages via requirements.txt, is not a good practice**. For this reason it is suggested to use Rastion & Qubots in a secure environment using `python -m venv` or `conda create --name my_rastion_env python=3.9`. Developing a sandbox environment & shareable object for qubots should definitely be in the future plans.\n\n### Chaining and Pipelines\n\nQubots also supports more advanced patterns:\n\n- **Independent Runs**: Use helper functions (in `optimizer_runner.py`) to run several optimizers independently on the same problem, then compare their results.\n\n- **Chained Refinement**: Sequentially refine a solution by passing the output of one optimizer as the initial solution for the next.\n\n- **Quantum-Classical Pipelines**: The `QuantumClassicalPipeline` class (and its helper function `create_quantum_classical_pipeline`) enables you to combine a quantum routine with a classical optimizer. For example, a quantum solver might generate a candidate solution which is then refined by a classical optimizer.\n\n### CLI Tools\n\nThe package includes a set of command-line utilities (via the `rastion` script) to:\n\n- **Create, Update, and Delete Repositories**: Easily manage GitHub repositories under the Rastion organization.\n- **Push Solver and Problem Code**: Automate the process of packaging your qubot (along with its configuration and dependencies) and pushing it to GitHub.\n- **Run Solvers Directly**: Load a solver and a problem from GitHub repositories and run the optimization in one command.\n\nFor example, to run a solver from the command line:\n\n```bash\nrastion run_solver Rastion/particle-swarm --problem-repo Rastion/max-cut\n```\n\nMost CLI tools need a verified github token to work. Currently this github token must be from a member of the Rastion org. We are working on a way to make Rastion automate this process (via a login option to the website).\n\n## Examples\n\nThe `examples/` directory contains some examples and test cases, including:\n\n- **Implemented tests**: Tests about the qubots library that have been tested to working locally.\n- **Testing to do**: Tests that fail locally.\n\n## Contributing\n\nWe welcome contributions to expand the qubots ecosystem! Here\u2019s how you can get involved:\n\n- **Report Issues**: If you encounter bugs or have feature suggestions, please open an issue on the GitHub repository.\n- **Submit Pull Requests**: Follow the coding guidelines and ensure that your changes include tests and documentation updates as needed.\n- **Share Your Qubots**: Have an innovative optimizer or an interesting problem formulation? Create a GitHub repo under the Rastion organization and share it via the Rastion hub.\n- **Improve Documentation**: Enhance guides, tutorials, and examples to help others get started with qubots.\n\nFor more detailed contribution guidelines, please refer to our `CONTRIBUTING.md`.\n\n## License\n\nThis project is licensed under the [Apache License 2.0](./LICENSE).\n\nBy leveraging the flexible design of qubots and the collaborative power of Rastion, you can rapidly prototype, share, and improve optimization solutions\u2014be it for classical problems, quantum algorithms, or hybrid systems.\n\n\nFor more information, contact gleonidas303@gmail.com.\n\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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