https://github.com/arec1b0/quantum-vqe-project

Optimization of Variational Quantum Eigensolver (VQE) circuits using gradient descent and parallel processing. Includes hyperparameter optimization through random search.
https://github.com/arec1b0/quantum-vqe-project

hyperparameter-tuning optimization parallel-processing quantum-computing vqe

Last synced: 26 days ago
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Optimization of Variational Quantum Eigensolver (VQE) circuits using gradient descent and parallel processing. Includes hyperparameter optimization through random search.

• Host: GitHub
• URL: https://github.com/arec1b0/quantum-vqe-project
• Owner: arec1b0
• License: mit
• Created: 2024-08-04T18:25:58.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-08-04T20:29:39.000Z (9 months ago)
• Last Synced: 2025-03-20T23:33:59.211Z (about 1 month ago)
• Topics: hyperparameter-tuning, optimization, parallel-processing, quantum-computing, vqe
• Language: HTML
• Homepage:
• Size: 1.34 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Quantum VQE Optimization Project

## Overview

This project focuses on optimizing Variational Quantum Eigensolver (VQE) circuits using gradient descent and parallel processing. It includes hyperparameter optimization through random search to identify the best parameters for the VQE circuits.

## Best Parameters

The best parameters identified through random search are:

- **Steps**: 200

- **Stepsize**: 0.1

- **Circuit**: default

## Results

The optimization process converged successfully, achieving a minimum cost value. The final report and graphs are saved in the `results` directory.

## Graphs and Visualizations

- ![Cost History Plot](results/Figure_1.png)

- ![Cost Distribution Histogram](results/Histogram.png)

- ![Moving Average Plot](results/Moving_Average.png)

- ![Boxplot of Cost](results/Boxplot.png)

- ![Density Plot of Cost](results/Density_Plot.png)

## Process Description

1. **Parameter Initialization**: The initial parameters were randomly initialized.

2. **Optimization**: The optimization process was performed using a gradient descent optimizer.

3. **Parallel Processing**: The optimization and analysis were done in parallel to improve performance.

4. **Analysis**: The results were analyzed using various statistical metrics and visualizations.

5. **Hyperparameter Search**: A random search was conducted to find the best hyperparameters.

## How to Run

1. **Clone the repository**:

    ```bash

    git clone https://github.com/dkrizhanovskyi/quantum-vqe-project.git

    cd quantum-vqe-project

    ```

2. **Install dependencies**:

    ```bash

    pip install -r requirements.txt

    ```

3. **Run the random search for hyperparameter optimization**:

    ```bash

    python random_search.py --config config.yaml

    ```

4. **Run the optimization with the best parameters**:

    ```bash

    python run_optimization.py --config config.yaml

    ```

## Configuration

The `config.yaml` file contains the configuration parameters for the optimization process. Update the parameters as needed.

### Example `config.yaml`

```yaml

optimization:

  steps: 200  # Best number of steps

  stepsize: 0.1  # Best step size

  circuit: default  # Best circuit type

  save_path: results/state.json

  load_path: ''

results_dir: results

parallel_processes: 4  # Number of parallel processes

 ```