vtc2026

Implementations for the conference VTC2026 about "A Quantum Method for Constrained Vehicle Dynamics and Green-Wave Optimization".

What's in here?

Here you can find

• config which contains configuration files such as gate_options.py and paths.py needed to specify the hyperparameters of the quantum machine learning algorithm and its training options.
• core which contains the core modules of the quantum machine learning algorithm, from the data_loader.py to the training.py utilities.
• run_pipeline.py is the file to execute in order to train the quantum machine learning algorithm and to select the best resultin model with best_instance.py.
• examples.ipynb which contains some step-by-step carried out examples of the model (see the companion article for more details).
• data.csv contains a classical simulation data baseline yielded by a traditional MPC.
• real_data.csv contains real measurements of an instrumented bus used as an experimental benchmark (see the companion article for more details).
• results_3-20.csv contains optimal results in the time interval $t\in [3,20]$ yielded by the best model obtained from run_pipeline.py and best_instance.py where the core/data_loader.py file containind the function load_dataset should be called with load_dataset(t_min=3, t_max=20).
• requirements.txt contains the requirements to reproduce the experiments we carried out.
• LICENCE is the MIT licence.

Use this repository

If you want to use the code in this repository in your projects, please cite explicitely our work, and

• Clone the repository with git clone https://github.com/leonardoLavagna/vtc2026
• Install the requirements with pip install -r requirements.txt

Contributing

We welcome contributions to enhance the functionality and performance of the models. Please submit pull requests or open issues for any improvements or bug fixes.

License

This project is licensed under the MIT License.

Citation

Cite this repository or one of the associated papers, such as:

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