{"id":27000075,"url":"https://github.com/arec1b0/quantum-vqe-project","last_synced_at":"2026-01-18T18:02:37.201Z","repository":{"id":251650464,"uuid":"838030273","full_name":"arec1b0/quantum-vqe-project","owner":"arec1b0","description":"Optimization of Variational Quantum Eigensolver (VQE) circuits using gradient descent and parallel processing. 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It includes hyperparameter optimization through random search to identify the best parameters for the VQE circuits.\r\n\r\n## Best Parameters\r\nThe best parameters identified through random search are:\r\n- **Steps**: 200\r\n- **Stepsize**: 0.1\r\n- **Circuit**: default\r\n\r\n## Results\r\nThe optimization process converged successfully, achieving a minimum cost value. The final report and graphs are saved in the `results` directory.\r\n\r\n## Graphs and Visualizations\r\n- ![Cost History Plot](results/Figure_1.png)\r\n- ![Cost Distribution Histogram](results/Histogram.png)\r\n- ![Moving Average Plot](results/Moving_Average.png)\r\n- ![Boxplot of Cost](results/Boxplot.png)\r\n- ![Density Plot of Cost](results/Density_Plot.png)\r\n\r\n## Process Description\r\n1. **Parameter Initialization**: The initial parameters were randomly initialized.\r\n2. **Optimization**: The optimization process was performed using a gradient descent optimizer.\r\n3. **Parallel Processing**: The optimization and analysis were done in parallel to improve performance.\r\n4. **Analysis**: The results were analyzed using various statistical metrics and visualizations.\r\n5. **Hyperparameter Search**: A random search was conducted to find the best hyperparameters.\r\n\r\n## How to Run\r\n1. **Clone the repository**:\r\n    ```bash\r\n    git clone https://github.com/dkrizhanovskyi/quantum-vqe-project.git\r\n    cd quantum-vqe-project\r\n    ```\r\n\r\n2. **Install dependencies**:\r\n    ```bash\r\n    pip install -r requirements.txt\r\n    ```\r\n\r\n3. **Run the random search for hyperparameter optimization**:\r\n    ```bash\r\n    python random_search.py --config config.yaml\r\n    ```\r\n\r\n4. **Run the optimization with the best parameters**:\r\n    ```bash\r\n    python run_optimization.py --config config.yaml\r\n    ```\r\n\r\n## Configuration\r\nThe `config.yaml` file contains the configuration parameters for the optimization process. Update the parameters as needed.\r\n\r\n### Example `config.yaml`\r\n```yaml\r\noptimization:\r\n  steps: 200  # Best number of steps\r\n  stepsize: 0.1  # Best step size\r\n  circuit: default  # Best circuit type\r\n  save_path: results/state.json\r\n  load_path: ''\r\nresults_dir: results\r\nparallel_processes: 4  # Number of parallel processes\r\n ```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farec1b0%2Fquantum-vqe-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farec1b0%2Fquantum-vqe-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farec1b0%2Fquantum-vqe-project/lists"}